<table><thead><tr><th>Species</th><th>Accuracy</th><th>Recall</th><th>1260A<br>6R-3 W,<br>18-20 cm</th><th>1260A<br>8R-5 W,<br>70-72 cm</th><th>1260A<br>13R-<br>5 W,<br>66-<br>68 cm</th><th>1260A<br>15R-<br>4 W,<br>69-<br>71 cm</th></tr></thead><tbody><tr><td><i>Eusyringium lagena</i></td><td>0.50</td><td>0.67</td><td>—</td><td>—</td><td>3</td><td>7</td></tr><tr><td><i>Eusyringium fistuligerum</i> group</td><td>0.86</td><td>0.75</td><td>9</td><td>5</td><td>2</td><td>0</td></tr><tr><td><i>Rhopalocanium ornatum</i> group</td><td>0.67</td><td>0.67</td><td>3</td><td>0</td><td>6</td><td>1</td></tr><tr><td><i>Cycladophora spatiosa</i> group</td><td>1.00</td><td>0.91</td><td>31</td><td>20</td><td>2</td><td>1</td></tr><tr><td><i>Anthocyrtis mespilus</i> group</td><td>0.78</td><td>0.64</td><td>14</td><td>3</td><td>10</td><td>3</td></tr><tr><td><i>Zeolithapium mitra</i></td><td>0.54</td><td>1.00</td><td>7</td><td>7</td><td>7</td><td>1</td></tr></tbody></table>

In general, the classes with good performance are *Elaphospyris didiceros* group (Plate 4.1.B), *Dictyomitra parva* (Plate 4.1.J), *Sethochytris triconiscus* (Plate 4.1.L), *Lithochytris vespertilio* (Plate 4.1.K), *Lychnocanoma bajunensis* (Plate 4.1.N), *Lychnocanium babylonis* group (Plate 4.1.M) and *Thyrsocyrts* (*Pentalocorys*) *triacantha* (Plate 4.1.Q). They are well-classified with few misclassified objects in their respective classes and they rarely appear in other classes.

The CNN was able to detect some of the true specimens of *Dendropyris stylophora* (Plate 4.1.A). However, in many cases some trissocyclids/cephalospyrids with long feet were also misclassified as *D. stylophora*. Since this class is quite rare, it is difficult to make any accurate estimate about the application accuracy. For the training iteration the CNN obtained an accuracy of about 70 %.

*Liriospyris clathrata* group is a simple single-segmented nassellarian with large pores on its cephalis (Plate 4.1.C). Occasionally, some specimens may be misclassified into higher-ranked taxonomic classes, but overall, it performs well. It has about 80 % accuracy in the CNN training.
Overall, the CNN was able to correctly identify all forms of *Dictyoprora amphora* (Plate 4.1.G) group, but many other broken and unusual radiolarian forms, including some *Dictyoprora* spp. and *Dictyoprora mongolfieri* (Plate 4.1.F), were confused with this species group. Although the majority of *D. mongolfieri* were identified in their true class, a smaller number of radiolarians or objects in the class were misclassified, indicating that these particular classes have been well-trained with a 90% accuracy.

Although some *Rhopalosyringium ? auriculaleporis* specimens (Plate 4.1.H) were identified correctly, there are still several other objects that are misclassified as this species. The same goes for *R. ? biauratum* (Plate 4.1.I), but since not many have been observed in our test samples, it is difficult to make any fair estimates for this particular species.

*Carpocanopsis ornata* group (Plate 4.1.P) is poorly trained and is largely misclassified in the training process, with a training accuracy of only ca. 30 %. This is likely due to its very simple, smooth outline that is similar to many other taxa.

*Stichopterigyum microporum* (Plate 4.1.O) is classified well, despite having many similarities with *Euctyrtidium levisaltarix*, a species that was not individualized in this study, but was included in the *Eucyrtidium* genus class. Occasionally, some of these species may be mixed up if there are no morphologically distinct morphotypes. However, in those cases where there are distinct morphotypes, they are classified correctly.

The training accuracy of *Lophophaena radians* group (Plate 4.1.E) was ca. 70%, although there are not many estimates on how well this species is classified in the new samples obtained.
*Rhabdolithis pipa* (Plate 4.1.D) was detected frequently in our samples. However, a lot of other particles also ended up being included in this class, alike some types of spumellarians, since *R. pipa* has only one simple segment and it does not display any radial symmetry and bears two very long spines.

*Lophocyrtis alauda* (Plate 4.2.A) is well detected in samples coming from 1260A-13R-5W, 66-68 cm and 1260A-15R-4W, 69-71 cm. The somewhat similar looking species, *Aphetocyrtis zamenhofi* (Meunier and Danelian 2023) was only found present in 1260A-15R-4W, 69-71 cm but other objects or specimens were also mistakenly classified as *A. zamenhofi*, even at samples in which they do not exist. The training accuracies are 60 % for *A. zamenhofi* and 75 % for *L. alauda*.

Not many specimens of *Calocyclas hispida* (Plate 4.2.O) have been trained by the CNN, and it is only in sample 1260A-8R-5W, 70-72 cm that they appeared more often; they were classified well, without having a lot of misidentified radiolaria or other objects appearing in that class.

We did not encounter many typical specimens of *Podocyrtis goetheana* (Plate 4.2.F), but mainly early/transitional forms that do not display the elongated abdomen with the typical long straight bars on the median row of pores. In any case, most transitional forms were classified as *Podocyrtis chalara* (Plate 4.2.G) and exceptionally as *P. goetheana*. Otherwise, *P. goetheana* has a unique morphology compared to the rest of radiolarians present in our samples and it was therefore trained very well with a perfect F1 score; both accuracy and precision were 100 %.

*Podocyrtis chalara* (Plate 4.2.G) is well classified and recognized by the CNN and is easy to detect in our test samples. However, when it comes to *Podocyrtis mitra* (Plate 4.2.H), our
samples contained transitional forms between *Podocyrtis sinuosa* (Plate 4.2.I) and *P. mitra* or *P. mitra* and *P. chalara*. In the latter case, most specimens we captured were actually closer to *P. chalara* than *P. mitra* and were therefore classified as *P. chalara* rather than *P. mitra*. In the studied material there were too few typical specimens of *P. sinuosa* and *P. mitra* to make up a clear mind, but most of them were transitional forms and the CNN had two specimens classified as *P. sinuosa* and one as *P. mitra*. Individually, *P. sinuosa* from other samples are rather well-detected, even though it happened to have samples with a lower abundance of *P. sinuosa*.

The CNN can detect well *Podocyrtis papalis* (Plate 4.2.J). However, some forms that do not belong to *P. papalis* were incorrectly classified, quite often as *Podocyrtis ampla* (Plate 4.2.K) or *Podocyrtis diamesa* (Plate 4.2.M). *Theocyrtis scolopax* was also found classified as *P. papalis*. There were not many specimens of *P. diamesa* in these samples; therefore, none was classified as *P. diamesa*, and the few specimens of *P. diamesa* were actually classified as *P. papalis*, which is logical since they are very similar (see also Carlsson et al., 2022). Finally, *P. ampla* was always confused with *P. papalis*, as regrettably the CNN could not correctly detect any single *P. ampla*.

*Podocyrtis phyxis* (Plate 4.2.L), an important biostratigraphic index species occurring only within a short interval, was trained in the CNN with an 89 % accuracy and a recall value of 88 %; however, it was never classified correctly into its own class in the new test set of four samples. Instead, it was frequently misidentified as *Thyrsocyrtis rhizodon* (Plate 4.2.P), which is understandable, given that both species have an equal number of segments and consist of a horn and feet (although they differ in size and shape) and are more or less barrel shaped.
Unfortunately, the CNN cannot detect size differences because all images are resized to the same dimensions.

In conclusion, the *Podocyrtoges* lineage, which includes *P. ampla*, *P. phyxis*, and *P. diamesa*, cannot be reliably detected in the new set of test samples using our currently trained CNN. The different morphospecies of this lineage are difficult to be identified correctly due to the frequent occurrence of transitional forms that look very similar to other taxa in our dataset. Although we have a sufficient dataset of these species, more data and adjustments to similar-looking classes may be necessary to allow the CNN to more clearly distinguish them with a high degree of accuracy, as humans are able to do.

We did not encounter any *Podocyrtis puellasinensis* (Plate 4.2.N) species but it was originally trained with an accuracy and precision of 80%.

*Eusyringium lagena* (Plate 4.2.R) can be easily detected by the CNN, and *Eusyringium fistuligerum* group (Plate 4.2.S) is occasionally misclassified as *L. vespertilio* (Plate 4.1.K) or *S. triconiscus* (Plate 4.1.L). This is understandable since their proximal parts (thorax, cephalis, and thick conical horn) look almost identical.

Not many specimens of *Rhopalocanium ornatum* group (Plate 4.2.T) were detected. This species was trained on a small number of specimens and therefore only obtained a training accuracy of about 70%. Some specimens of the *R. ornatum* group were found in its true class but were also appearing in other species and higher taxonomic leveled classes, which implies a lower recall number.
Both *Cycladophora spatiosa* group (Plate 4.1.Q) and *Anthocyrtis mespilus* group (Plate 4.1.R) could be detected well with a high accuracy in the samples in which they existed. In other samples, they had a lower prediction accuracy with more specimens incorrectly classified as either *A. spatiosa* group or *A. mespilus* group.

The classification of *Zeolithapium mitra* (Plate 4.1.S) is not reliable due to its insufficient training dataset, which comprises only a small number of images. As a result, many broken radiolarians with large pores are frequently misidentified as *Z. mitra*, despite the fact that this species is characterized by large pores with a more conical shape.

The results we obtained suggest that many of the classes we distinguished may be confidently used in future applications (biostratigraphic or paleoceanographic). Well distinct species that the CNN can easily detect in whole assemblage analyses have a low recall value and appear rarely in wrong classes. As in many cases, the presence/absence of an index species is sufficient for biostratigraphy, the automated classification of targeted species in whole assemblage studies described in this paper, enables us to quickly confirm the correct classification of species and thus opens new perspectives for the application of Artificial Intelligence to radiolarian biostratigraphic studies. Apart from the 39 targeted species, many of the other classes had a worse performance; indeed, many half-complete or blurry specimens were classified as other objects. This makes it difficult for the moment to fully trust the CNN classification for counting all radiolarian species in order to get information about their relative abundances.

## 4.5 Conclusions

The newly established dataset of middle Eocene tropical radiolarians is well adjusted to fit a CNN. We obtained a high training accuracy of 86.6 % for its training in a CNN.
We evaluated the performance of our trained Convolutional Neural Network (CNN) on new tests and compared it to human performance, and obtained a testing accuracy of about 75.69 %.

We thereafter specifically focused on 39 different species which the CNN demonstrated notable success in accurately identifying those species that had been well-trained.

In order to obtain an acceptable accuracy of the CNN for further studies, the labeling of classes was also revised to groups or separate taxa and reached the best compromise between CNN accuracy and consistent taxonomy. For example, when two morphologically close species or subspecies where often confused by the CNN, we found it better to fuse them together in an acceptable taxonomic framework, unless they were individually of biostratigraphic importance, instead of artificially biasing the CNN accuracy by often mistaking these two taxa with each other, in the same way as they may be confused by an operator.

This has proved to be an efficient way, both in speed and easiness, to quickly see what kind of radiolarian species exist and how many of them. However, since we focused only on a few classes, we cannot compare the relative abundance with certain taxa in relation to all radiolarians yet, but with improved methods and building a stronger dataset, it will be possible to get a closer estimate of the relative abundance of many taxa. This also highlights the importance of building good taxonomic datasets.

Overall, applying automatic image classification to the studied samples is time-saving, particularly for detecting the presence of the selected nassellarian species. This approach eliminates the need to manually count and track by an operator the targeted taxa present in a sample and avoids the risk of identification bias between different operators.
## Data availability

Microscope slides from Leg 207, Hole 1258A, 1259A and 1260A, which were used for our dataset and application to a trained CNN, are stored at the University of Lille, France. The dataset (https://doi.org/10.57745/E9YXW6, Carlsson, 2023) is published in the University of Lille repository at Recherche Data Gouv.

## Acknowledgments

This study was partly funded by the French government through the program "Investissements d'avenir" (I-ULNE SITE/ANR-16-IDEX-0004 ULNE) managed by the National Research Agency. It also received funding from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement no. 847568. It was also supported by UMR 8198 Evo-Eco-Paléo and IRCICA (CNRS and Univ. Lille USR-3380).

A special thanks to Sylvie Regnier for helping to prepare the new samples by removal of non-siliceous materials, and also many thanks to Ross Marchant for the quick reparation of bugs in ParticleTrieur.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at
https://doi.org/10.1016/j.marmicro.2023.102268.
# CHAPTER 4: Subchapter 2 - Initial middle Eocene radiolarians
dataset from the tropical Atlantic (ODP Leg 207) partly classified
by K-means clustering

## 4.1 (2) Introduction

In our initial dataset, we attempted to classify all other particles or less interesting or resisting radiolarians by using unsupervised methods, like K-means clustering (Lloyd, 1957; MacQueen, 1967). K-means clustering is an unsupervised learning algorithm that classifies objects into a specified number of clusters (k) depending on similar data points. The idea is to divide a set of data points into k clusters, in such a way that the sum of the squared distances between each data point and its nearest cluster center is minimized. This is accomplished by an iterative process. In this process, each data point is assigned to the closest cluster center, and the center is then updated to reflect the average position of the data points assigned to it. This continues iteratively until no more changes occur in the centroids or until the iterations are complete. For images, each pixel corresponds to a data point and the k-means clustering algorithm tries to cluster similar pixels together.

The main difference between KNN and K-means clustering is that KNN is supervised while K-means clustering is unsupervised. KNN predicts the class label of a new data point which is based on the majority label of its k-nearest neighbors, while K-means clustering partitions a set of data points into k clusters based on their similarity. As in Subchapter 1, this dataset was trained in a ResNet50 (He et al., 2016) Convolutional Neural Network (CNN).
## 4.2 (2) Results

The result of this was not very satisfactory, especially since the majority of the images were not of interest and could therefore be overtrained and destroy the result of the classes that we are interested in. The overall accuracy for the first trial was about 61.4%, with a precision of 55.1% and 63.9% in recall.

## 4.3 (2) Discussion

In the first trial, we included a total of 163 classes, some of which were classified in a supervised way, such as 79 classes of Nassellaria and 7 classes of Spumellaria, all of which were double-checked by two other radiolarian taxonomists.

Our focus was mainly on classifying Nassellaria to an approved taxonomic rank, with a focus on 37 taxa at the species level, some of which are important in biostratigraphy. We tried to have at least 30 images per class, although some very abundant classes had a few hundred images in their class. Even though we were sure about a class sometimes, due to their low abundance, they were placed into higher taxonomic ranks. For the rest of Nassellaria, they were placed in higher ranks, for which we were comfortable with their identification, either at genus, family, superfamily, or even order level; some classes were even considered as "bin classes".

The remaining Spumellaria, together with other objects, were classified using unsupervised K-means clustering. The reason why Spumellaria are not more finely classified is twofold. The first reason is that Spumellarian identification is dependent on the morphology of the inner shell, which is not very visible when working with stacked images; the second reason is time constraints. However, since most Spumellaria have radial symmetry, they work better for K-
means clustering, as clusters are also dependent on orientation and many Spumellaria look
identical at any orientation they appear in.

By training the entire dataset with the high number of unsupervised classified “bin” classes, the
CNN did not perform that well, since the majority of the dataset consisted of “bin” classes rather
than the radiolaria of interest, which could create an imbalance in the dataset. The network
might focus more on the “bin” classes due to their higher representation, ignoring the essential
classes, which could affect the overall performance of the model. However, after revision based
on the first results, changes in the dataset were made by removing a lot of the trash classes
which are not of interest removing about 25,000 images, and just kept a few classes from those
“bin” classes consisting of other fragments. The Spumellarians were better organized, although
their taxa name may not be perfectly correct. For the Nassellaria, some higher taxonomic ranks
consisted of too many variations in their datasets, which caused some problems. Therefore,
many Nassellaria classes were divided further into lower-ranked classes, we still kept most of
the species classes just gained a few more. This time we tried to obtain at least ten specimens
per class instead of the previous 30 specimens per class. Taxa classes including very few
numbers of specimens that were often misclassified as another class were often fused forming
a new class and vice versa, classes with too much variation were split into several more classes.

Apart from this study, we initially tried to classify everything, both using K-means clustering
and Spiking Time Dependent Plasticity (STDP) (Masquelier and Thorpe, 2007). However, we
did not properly succeed due to a lot of irregularities in the images, such as differences in
contrasts, touching objects, bubble inclusion (giving a lot of unnecessary pixel values), and
Nassellaria being randomly rotated and having a bilateral symmetry. This attempt did not
improve the sorting of images, but it could work to group Spumellaria to some degree.
Spiking Time Dependent Plasticity (STDP) is a new unsupervised learning algorithm that is still in its early research stage. It is inspired by the biological brain and focuses on the strength of the different neural signals.
Appendix A: Initial list of all the classes for the radiolarian image database from
the Demerara Rise at the Tropical Atlantic Ocean.

ACANTHODESMIOIDEA CEPHALOSPYRIDIDAE Gen indet sp A

ACANTHODESMIOIDEA CEPHALOSPYRIDIDAE Gen indet sp B

ACANTHODESMIOIDEA group B

ACANTHODESMIOIDEA CEPHALOSPYRIDIDAE *Dendrospyris stylophora*

ACANTHODESMIOIDEA CEPHALOSPYRIDIDAE *Desmospyris obtusus* group

ACANTHODESMIOIDEA CEPHALOSPYRIDIDAE *Elaphospyris didiceros* group

ACANTHODESMIOIDEA CEPHALOSPYRIDIDAE group A

ACANTHODESMIOIDEA CEPHALOSPYRIDIDAE group B

ACANTHODESMIOIDEA CEPHALOSPYRIDIDAE group C

ACANTHODESMIOIDEA CEPHALOSPYRIDIDAE group D

ACANTHODESMIOIDEA CEPHALOSPYRIDIDAE *Liriospyris clathrata* group

ACANTHODESMIOIDEA CEPHALOSPYRIDIDAE *Petalospyris* spp

ACANTHODESMIOIDEA group A

ACANTHODESMIOIDEA group C

ACANTHODESMIOIDEA STEPHANIIDAE *Zygocircus buetschli*

ACANTHODESMIOIDEA STEPHANIIDAE *Zygocircus* spp

ARCHAEODICTYOMITROIDEA ARCHAEDICTYOMITRIDAE *Dictyomitra parva*

ARTOSTROBIOIDEA ARTOSTROBIIDAE *Dictyoprora amphora* group

ARTOSTROBIOIDEA ARTOSTROBIIDAE *Dictyoprora mongolfieri*

ARTOSTROBIOIDEA ARTOSTROBIIDAE *Dictyoprora* spp

ARTOSTROBIOIDEA ARTOSTROBIIDAE *Siphocampe* spp

ARTOSTROBIOIDEA group A

CARPOCANIOIDEA CARPOCANIIDAE *Carpocanopsis ornata* group

CLADE G group A

EUCYRTIDIOIDEA EUCYRTIDIIDAE *Eucyrtidium* spp

EUCYRTIDIOIDEA EUCYRTIDIIDAE *Stichopterygium microporum*

LITHOCHYTRIDOIDEA LITHOCYRTIDIDAE *Lithochyrtis* spp

LITHOCHYTRIDOIDEA LITHOCYRTIDIDAE group A

LITHOCHYTRIDOIDEA LITHOCYRTIDIDAE *Lychnocanissa bajunensis*

LITHOCHYTRIDOIDEA LITHOCYRTIDIDAE *Lychnocanium babylonis* group

NASSELLARIA group A

NASSELLARIA group B

NASSELLARIA group C

NASSELLARIA group D

NASSELLARIA group E

NASSELLARIA MIXED KNOWN

NASSELLARIA UNKNOWN

Other

Other (10)

Other (11)

Other (12)

Other (13)

Other (14)
Other (15)

Other (16)

Other (17)

Other (18)

Other (19)

Other (2)

Other (20)

Other (21)

Other (22)

Other (23)

Other (24)

Other (25)

Other (26)

Other (27)

Other (28)

Other (29)

Other (3)

Other (30)

Other (31)

Other (32)

Other (33)

Other (34)

Other (35)

Other (36)

Other (37)

Other (38)

Other (39)

Other (4)

Other (40)

Other (41)

Other (42)

Other (43)

Other (44)

Other (45)

Other (46)

Other (47)

Other (48)

Other (49)

Other (5)

Other (50)

Other (6)

Other (7)

Other (8)

Other (9)

PLAGIACANTHOIDEA - DICTYOCRYPHALIDAE - *Dictyocryphalus capito* gro

PLAGIACANTHOIDEA CERATOCYRTIDAE *Ceratocyrtis* spp
PLAGIACANTHOIDEA DICTYOCRYPHALIDAE *Dictyocryphalus radians* group

PLAGIACANTHOIDEA group A

PLAGIACANTHOIDEA LOPHOPHANIDAE group A

PLAGIACANTHOIDEA LOPHOPHANIDAE group B

PLAGIACANTHOIDEA PLAGIACANTHIDAE *Rhabdolithis pipa*

PLECTOPYRAMIDOIDEA PLECTOPYRAMIDIEA group A

PTEROCORYTHOIDEA LOPHOCYRTIDAE group A

PTEROCORYTHOIDEA LOPHOCYRTIDAE group B

PTEROCORYTHOIDEA group A

PTEROCORYTHOIDEA group B

PTEROCORYTHOIDEA LOPHOCYRTIDAE *Aphetocyrtis zamenhofi*

PTEROCORYTHOIDEA LOPHOCYRTIDAE *Lophocyrtis alauda*

PTEROCORYTHOIDEA LOPHOCYRTIDAE *Paralampterium scolopax*

PTEROCORYTHOIDEA PTEROCORYTHIDAE *Albatrossidium* spp

PTEROCORYTHOIDEA PTEROCORYTHIDAE *Calocyclas hispida*

PTEROCORYTHOIDEA PTEROCORYTHIDAE *Calocycloma ampulla*

PTEROCORYTHOIDEA PTEROCORYTHIDAE *Phormocyrtis embolum*

PTEROCORYTHOIDEA PTEROCORYTHIDAE *Phormocyrtis* group

PTEROCORYTHOIDEA PTEROCORYTHIDAE *Phormocyrtis lazari*

PTEROCORYTHOIDEA PTEROCORYTHIDAE *Podocyrtis Lampterium chalara*

PTEROCORYTHOIDEA PTEROCORYTHIDAE *Podocyrtis Lampterium goetheana*

PTEROCORYTHOIDEA PTEROCORYTHIDAE *Podocyrtis Lampterium mitra*

PTEROCORYTHOIDEA PTEROCORYTHIDAE *Podocyrtis Lampterium sinuosa*

PTEROCORYTHOIDEA PTEROCORYTHIDAE *Podocyrtis papalis*

PTEROCORYTHOIDEA PTEROCORYTHIDAE *Podocyrtis Podocyrtoges ampla*

PTEROCORYTHOIDEA PTEROCORYTHIDAE *Podocyrtis Podocyrtoges diamesa*

PTEROCORYTHOIDEA PTEROCORYTHIDAE *Podocyrtis Podocyrtoges phyxis*

PTEROCORYTHOIDEA THEOCOTYLIDAE *Axocorys* spp

PTEROCORYTHOIDEA THEOCOTYLIDAE group A

PTEROCORYTHOIDEA THEOCOTYLIDAE *Theocorys* spp

PTEROCORYTHOIDEA THEOCOTYLIDAE *Thyrsocyrtis rhizodon* group

PTEROCORYTHOIDEA THEOCOTYLIDAE *Pentalocorys triacantha* group

PTEROCORYTHOIDEA THEOPERIDAE *Eusyringium fistuligerum* group

PTEROCORYTHOIDEA THEOPERIDAE *Eusyringium lagena*

PTEROCORYTHOIDEA THEOPERIDAE group A

PYLOBOTRYDOIDEA PYLOBOTRYDIDAE group A

Spumellaria

SPUMELLARIA STYLOSPHAEROIDEA - STYLATRACTIDAE *Stylophaera* spp

Spumellaria (10)

Spumellaria (14)

Spumellaria (15)

Spumellaria (17)
Spumellaria (18)

Spumellaria (2)

Spumellaria (21)

Spumellaria (24)

Spumellaria (26)

Spumellaria (27)

Spumellaria (28)

Spumellaria (29)

Spumellaria (3)

Spumellaria (30)

Spumellaria (5)

Spumellaria (6)

Spumellaria (9)

SPUMELLARIA LITHOCYCLIOIDEA LITHOCYCLIIDAE *Lithocyclia ocellus*

SPUMELLARIA LITHOCYCLIOIDEA PHACODISCIDAE *Periphaena decora*

SPUMELLARIA LITHOCYCLOIDEA LITHOCYCLIIDAE *Heliosestrum* spp

SPUMELLARIA PHORTICIOIDEA HISTIASTRIDAE *Histiastrum* group

STYLOSPHAEROIDEA STYLATRACTIDAE *Zeolithapium mitra*

STYLOSPHAEROIDEA STYLATRACTIDAE *Zeolithapium* spp

THEOPILIOIDEA ANTHOCYRTIDIDAE *Anthocyrtis* group

THEOPILIOIDEA ANTHOCYRTIDIDAE *Anthocyrtis mespilus* group

THEOPILIOIDEA ANTHOCYRTIDIDAE *Anthocyrtis spatiosa*

TREMATODISCOIDEA A

TREMATODISCOIDEA B

TREMATODISCOIDEA C

TREMATODISCOIDEA D

TREMATODISCOIDEA E

TREMATODISCOIDEA F

TREMATODISCOIDEA G

TREMATODISCOIDEA H

TREMATODISCOIDEA I
# CHAPTER 5: Using convolutional neural network for improving the biostratigraphy on middle Eocene radiolaria from ODP Leg 207, Site 1260

## Abstract

Radiolaria among other microfossils are useful to date sediments by studying the species occurrences shifting between different intervals. Biostratigraphic work demands a lot of time and effort and the need for a taxonomic expert; therefore, automatic image recognition can simplify the workflow. Here, we worked with images from samples photographed by an automatic microscope and used an already trained CNN, to automatically classify 90,237 images coming from 23 different samples. Despite using far fewer samples than the original biostratigraphic work on the same ODP Leg 207 Site 1260 and a limited amount of sediment to avoid touching objects, so that the segmentation software can crop each particle into individual images, our results show that in most cases the species ranges found in this study compare well to the results of an earlier, classically conducted, biostratigraphic study. It is thus established that stratigraphic ranges may be well recognized by the CNN, and in some cases, the CNN could expand the ranges. However, due to the low number of sediments together with some more poorly trained species, the ranges of rare species cannot always be detected by the CNN. Furthermore, the biostratigraphic species trained were also revised, showing their potential usefulness in further studies. Some classes are very well-trained and could be used for automatic image recognition without any further taxonomic expertise, while other species with high recall values could be applied to semi-automatic image recognition for biostratigraphy
with some revision. A few classes still need more training before being reliably used for
biostratigraphic applications.

**5.1 Introduction**

Radiolaria are microscopic unicellular heterotrophic eukaryotic plankton organisms, living exclusively in marine environments. It is made of aesthetically pleasing amorphous silica test or shells and has existed since the Cambrian age. Radiolaria are commonly used in biostratigraphy in areas where there are better studied calcareous microfossils such as foraminifera and nannofossils are absent.

Sediments from the Ocean Drilling Program (ODP) Leg 207, drilled at the Demerara Rise of the coast off Suriname in the tropical Atlantic Ocean; are enriched in silica resulting in highly preserved radiolarians. The middle Eocene sequence is almost entirely complete, with estimations of about 500 species of radiolarians.

ODP Site 1260 has been dated using both magnetostratigraphic and cyclostratigraphic methods (Westerhold and Röhl, 2013). A recent biostratigraphic work was obtained on this site by Meunier and Danelian (2022), in which the limits of radiolarian biozonations of RP16, RP15, RP14, and RP13 were determined, along with suggestions for new biozonations of RP15a and b; RP14a and b; and RP13a and b.

In recent years, a lot of studies have applied Artificial Intelligence (AI) to micropaleontology, especially using Convolutional Neural Networks (CNNs), which is a type of AI specifically designed for visual recognition of videos and images. Carlsson et al., (2023), trained a large image dataset from samples coming from ODP Leg 207, from Site 1258, 1259, and 1260 with
96 classes representing a large part of the entire middle Eocene assemblages from the tropical Atlantic, obtaining thus a training accuracy of about 86%; and a testing accuracy of ca 76%.

This study is a continuation of Carlsson et al. (2023), by using a real biostratigraphic approach to test how a trained CNN works in identifying biostratigraphic events compared to another recent biostratigraphic work made by Meunier and Danelian (2022). Here, we only focused on biostratigraphic important species that have been trained by a CNN, using a Resnet50 architecture. We commented about the ranges of species that the CNN could detect which was manually supervised afterward and we also recognized which classes are more reliable than others for fully automatic or semi-automatic image recognition that eventually could be used for other taxonomic experts or non-taxonomists.

## 5.2 Materials and Methods

### 5.2.1 Materials

Deep sea sediment samples are retrieved from the Ocean Drilling Program (ODP) Leg 207, Site 1260, at the Demerara Rise, off the coast off Suriname. A total of 23 different middle Eocene samples (Table 5.1), with one sample containing eight small coverslips, which is 1.2 x 1.2 cm (Tetard et al., 2020), was used in this study.

<table><caption>**Table 5.1.** List of samples used for the image recognition.</caption><thead><tr><th>EXPEDITION</th><th>SITE</th><th>HOLE</th><th>CORE</th><th>CORE TYPE</th><th>SECTION</th><th>TOP DEPTH</th><th>BOTTOM DEPTH</th><th>MBSF TOP</th><th>MCD TOP</th><th>AGE</th></tr></thead><tbody><tr><td>207</td><td>1260</td><td>A</td><td>6</td><td>R</td><td>3</td><td>18</td><td>20</td><td>41.38</td><td>41.38</td><td></td></tr><tr><td>207</td><td>1260</td><td>A</td><td>6</td><td>R</td><td>4</td><td>20</td><td>22</td><td>42.9</td><td>42.9</td><td></td></tr><tr><td>207</td><td>1260</td><td>A</td><td>6</td><td>R</td><td>4</td><td>119</td><td>121</td><td>43.89</td><td>43.89</td><td>40,070516</td></tr><tr><td>207</td><td>1260</td><td>A</td><td>6</td><td>R</td><td>5</td><td>63</td><td>65</td><td>44.83</td><td>44.83</td><td>40,112968</td></tr><tr><td>207</td><td>1260</td><td>A</td><td>7</td><td>R</td><td>1</td><td>69</td><td>71</td><td>48.19</td><td>48.29</td><td>40,298900</td></tr><tr><td>207</td><td>1260</td><td>A</td><td>7</td><td>R</td><td>3</td><td>18</td><td>20</td><td>50.68</td><td>50.78</td><td></td></tr><tr><td>207</td><td>1260</td><td>A</td><td>8</td><td>R</td><td>5</td><td>70</td><td>72</td><td>63.9</td><td>64</td><td></td></tr></tbody></table>
<table><tr><td>207</td><td>1260</td><td>A</td><td>8</td><td>R</td><td>6</td><td>65</td><td>67</td><td>65.35</td><td>65.45</td></tr><tr><td>207</td><td>1260</td><td>A</td><td>9</td><td>R</td><td>1</td><td>64</td><td>66</td><td>67.54</td><td>66.54</td></tr><tr><td>207</td><td>1260</td><td>A</td><td>9</td><td>R</td><td>2</td><td>64</td><td>66</td><td>69.04</td><td>68.04</td></tr><tr><td>207</td><td>1260</td><td>A</td><td>9</td><td>R</td><td>4</td><td>68</td><td>70</td><td>72.08</td><td>71.08</td></tr><tr><td>207</td><td>1260</td><td>A</td><td>10</td><td>R</td><td>4</td><td>64</td><td>66</td><td>81.74</td><td>80.84</td></tr><tr><td>207</td><td>1260</td><td>A</td><td>11</td><td>R</td><td>3</td><td>61</td><td>63</td><td>89.91</td><td>88.53</td></tr><tr><td>207</td><td>1260</td><td>A</td><td>11</td><td>R</td><td>7</td><td>68</td><td>70</td><td>95.98</td><td>94.6</td></tr><tr><td>207</td><td>1260</td><td>A</td><td>13</td><td>R</td><td>3</td><td>70</td><td>72</td><td>109.3</td><td>107.32</td></tr><tr><td>207</td><td>1260</td><td>A</td><td>13</td><td>R</td><td>4</td><td>68</td><td>70</td><td>110.78</td><td>108.8</td></tr><tr><td>207</td><td>1260</td><td>A</td><td>13</td><td>R</td><td>5</td><td>66</td><td>68</td><td>112.26</td><td>110.28</td></tr><tr><td>207</td><td>1260</td><td>A</td><td>13</td><td>R</td><td>6</td><td>68</td><td>70</td><td>113.78</td><td>111.8</td></tr><tr><td>207</td><td>1260</td><td>A</td><td>14</td><td>R</td><td>5</td><td>65</td><td>67</td><td>121.95</td><td>120.27</td></tr><tr><td>207</td><td>1260</td><td>A</td><td>15</td><td>R</td><td>1</td><td>69</td><td>71</td><td>125.59</td><td>123.78</td></tr><tr><td>207</td><td>1260</td><td>A</td><td>15</td><td>R</td><td>2</td><td>68</td><td>70</td><td>127.08</td><td>125.27</td></tr><tr><td>207</td><td>1260</td><td>A</td><td>15</td><td>R</td><td>3</td><td>69</td><td>71</td><td>128.59</td><td>126.78</td></tr><tr><td>207</td><td>1260</td><td>A</td><td>15</td><td>R</td><td>4</td><td>69</td><td>71</td><td>130.09</td><td>128.28</td></tr></table>

### 5.2.2 Methods

Samples were prepared using the same technique described in Tetard et al. (2020), and photographed in a Nikon automatic microscope, see Carlsson et al. (2023) for more details concerning the microscopy. Furthermore, the image processing is described in both Tetard et al. (2020) and Carlsson et al. (2023).

From all the 23 samples, 90,237 segmented images were recovered, and between 1,700 and 9,000 individual images for each sample. These were then analyzed by a trained CNN (Carlsson et al., 2023). The automatic image classification took part entirely in the software ParticleTrieur (Marchant et al., 2020) v.3.0.4. Images were pre-prepared to be equally in a 256-pixel size, from previously having 0.36 µm/pixel resolution. A confidence score/threshold value of 0.3 was preselected, enabling more images to be classified, despite leading to a lot of images being classified incorrectly (Carlsson et al., 2023). The average running time for the image
classification in ParticleTrieur was about two images per second using a computer standard intel Core i5, 10th generation, hardware, taking less than two working days to finalize. A total of 90 078 images were classified and important biostratigraphic species were double-checked so that they ended up in the correct class.

## 5.3 Results

### 5.3.1 CNN prediction and revision

In ParticleTrieur, data such as sample counts were exported, showing species occurrence predicted by the CNN along with the samples, which is chronologically sorted starting at the youngest age. We have also removed all classes which are not known to be of biostratigraphic importance (see Table 5.2). Apart from that, images can be exported as well and are sorted at their predicted classes keeping the name of which sample they come from.

<table><caption>**Table 5.2.** Biostratigraphic important species that has been automatically recognized by a trained CNN from a batch of ca. 90 000 images covering 23 samples. The pale-green colour transition is the number of specimens/images recognized by the CNN, green being the highest.</caption><thead><tr><th>Sample</th><th>D. parva</th><th>R? biaurata</th><th>C. ornatum group</th><th>L. vespertilio</th><th>L. triconiscus</th><th>L. babylonis group</th><th>L. a radians group</th><th>A. zamenhofi</th><th>L. alauda</th><th>T. scolopax</th><th>P. embolum</th><th>P. lazari</th><th>P. goetheana</th><th>P. mitra group</th><th>P. sinuosa</th><th>P. alpinum</th><th>E. lagena</th><th>Z. mitra</th></tr></thead><tbody><tr><td>1260A-6R-3W-18-20</td><td>3</td><td>10</td><td>0</td><td>25</td><td>1</td><td>0</td><td>2</td><td>8</td><td>9</td><td>1</td><td>0</td><td>20</td><td>10</td><td>15</td><td>2</td><td>2</td><td>2</td><td>0</td><td>0</td><td>0</td><td>48</td></tr><tr><td>1260A-6R-4W-20-22</td><td>0</td><td>8</td><td>2</td><td>10</td><td>18</td><td>4</td><td>3</td><td>4</td><td>15</td><td>1</td><td>0</td><td>15</td><td>2</td><td>44</td><td>7</td><td>10</td><td>3</td><td>2</td><td>3</td><td>1</td><td>2</td><td>71</td></tr><tr><td>1260A-6R-4W-119-121</td><td>10</td><td>4</td><td>0</td><td>31</td><td>0</td><td>2</td><td>5</td><td>15</td><td>10</td><td>0</td><td>1</td><td>13</td><td>5</td><td>11</td><td>0</td><td>9</td><td>1</td><td>0</td><td>2</td><td>1</td><td>0</td><td>34</td></tr><tr><td>1260A-6R-5W-63-65</td><td>2</td><td>11</td><td>0</td><td>22</td><td>6</td><td>8</td><td>3</td><td>4</td><td>26</td><td>2</td><td>1</td><td>21</td><td>4</td><td>35</td><td>3</td><td>4</td><td>8</td><td>0</td><td>1</td><td>0</td><td>0</td><td>104</td></tr><tr><td>1260A-7R-1W-69-71</td><td>1</td><td>0</td><td>0</td><td>1</td><td>21</td><td>20</td><td>1</td><td>0</td><td>7</td><td>0</td><td>0</td><td>7</td><td>1</td><td>86</td><td>5</td><td>5</td><td>0</td><td>0</td><td>1</td><td>4</td><td>0</td><td>73</td></tr><tr><td>1260A-7R-3W-18-20</td><td>0</td><td>9</td><td>1</td><td>0</td><td>9</td><td>15</td><td>0</td><td>9</td><td>18</td><td>0</td><td>1</td><td>20</td><td>6</td><td>41</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>88</td></tr></tbody></table>
<table><tr><td>1260A-8R-5W-70-72</td><td>1</td><td>4</td><td>3</td><td>0</td><td>3</td><td>12</td><td>5</td><td>1</td><td>13</td><td>0</td><td>0</td><td>4</td><td>4</td><td>38</td><td>1</td><td>5</td><td>2</td><td>0</td><td>0</td><td>3</td><td>1</td><td>43</td></tr><tr><td>1260A-8R-6W-65-67</td><td>3</td><td>2</td><td>5</td><td>1</td><td>2</td><td>4</td><td>5</td><td>1</td><td>5</td><td>0</td><td>1</td><td>6</td><td>5</td><td>18</td><td>8</td><td>9</td><td>1</td><td>0</td><td>0</td><td>5</td><td>0</td><td>24</td></tr><tr><td>1260A-9R-1W-64-66</td><td>0</td><td>0</td><td>3</td><td>1</td><td>3</td><td>3</td><td>6</td><td>0</td><td>3</td><td>0</td><td>0</td><td>19</td><td>2</td><td>30</td><td>7</td><td>29</td><td>6</td><td>0</td><td>2</td><td>0</td><td>0</td><td>35</td></tr><tr><td>1260A-9R-2W-64-66</td><td>5</td><td>2</td><td>5</td><td>3</td><td>2</td><td>5</td><td>6</td><td>0</td><td>9</td><td>0</td><td>1</td><td>4</td><td>1</td><td>8</td><td>2</td><td>6</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>18</td></tr><tr><td>1260A-9R-4W-68-70</td><td>9</td><td>32</td><td>3</td><td>15</td><td>1</td><td>6</td><td>8</td><td>9</td><td>31</td><td>1</td><td>2</td><td>24</td><td>35</td><td>6</td><td>1</td><td>3</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>14</td></tr><tr><td>1260A-10R-4W-64-66</td><td>9</td><td>23</td><td>7</td><td>20</td><td>10</td><td>11</td><td>23</td><td>27</td><td>57</td><td>2</td><td>3</td><td>56</td><td>2</td><td>7</td><td>1</td><td>12</td><td>4</td><td>0</td><td>1</td><td>0</td><td>1</td><td>52</td></tr><tr><td>1260A-11R-3W-61-63</td><td>0</td><td>11</td><td>0</td><td>4</td><td>18</td><td>1</td><td>7</td><td>4</td><td>19</td><td>7</td><td>19</td><td>19</td><td>6</td><td>0</td><td>0</td><td>4</td><td>1</td><td>2</td><td>1</td><td>0</td><td>0</td><td>23</td></tr><tr><td>1260A-11R-7W-68-70</td><td>2</td><td>40</td><td>5</td><td>21</td><td>1</td><td>2</td><td>9</td><td>8</td><td>3</td><td>4</td><td>4</td><td>20</td><td>10</td><td>0</td><td>0</td><td>0</td><td>2</td><td>0</td><td>0</td><td>0</td><td>1</td><td>12</td></tr><tr><td>1260A-13R-3W-70-72</td><td>1</td><td>8</td><td>5</td><td>2</td><td>3</td><td>0</td><td>26</td><td>5</td><td>16</td><td>9</td><td>8</td><td>30</td><td>5</td><td>13</td><td>1</td><td>17</td><td>5</td><td>2</td><td>0</td><td>1</td><td>3</td><td>28</td></tr><tr><td>1260A-13R-4W-68-70</td><td>0</td><td>3</td><td>1</td><td>1</td><td>4</td><td>3</td><td>12</td><td>2</td><td>7</td><td>12</td><td>1</td><td>8</td><td>0</td><td>5</td><td>0</td><td>5</td><td>5</td><td>9</td><td>0</td><td>0</td><td>1</td><td>23</td></tr><tr><td>1260A-13R-5W-66-68</td><td>2</td><td>23</td><td>3</td><td>13</td><td>3</td><td>0</td><td>17</td><td>8</td><td>7</td><td>8</td><td>3</td><td>24</td><td>7</td><td>1</td><td>0</td><td>1</td><td>1</td><td>5</td><td>0</td><td>1</td><td>5</td><td>15</td></tr><tr><td>1260A-13R-6W-68-70</td><td>0</td><td>5</td><td>0</td><td>7</td><td>8</td><td>0</td><td>12</td><td>5</td><td>10</td><td>10</td><td>2</td><td>30</td><td>2</td><td>0</td><td>0</td><td>4</td><td>7</td><td>4</td><td>1</td><td>3</td><td>1</td><td>11</td></tr><tr><td>1260A-14R-5W-65-67</td><td>9</td><td>26</td><td>0</td><td>12</td><td>3</td><td>2</td><td>4</td><td>15</td><td>4</td><td>5</td><td>8</td><td>15</td><td>5</td><td>0</td><td>0</td><td>1</td><td>2</td><td>1</td><td>1</td><td>0</td><td>1</td><td>11</td></tr><tr><td>1260A-15R-1W-69-71</td><td>6</td><td>13</td><td>1</td><td>1</td><td>13</td><td>1</td><td>7</td><td>2</td><td>36</td><td>3</td><td>9</td><td>26</td><td>4</td><td>2</td><td>2</td><td>6</td><td>3</td><td>2</td><td>0</td><td>0</td><td>10</td><td>29</td></tr><tr><td>1260A-15R-2W-68-70</td><td>16</td><td>15</td><td>2</td><td>18</td><td>20</td><td>3</td><td>14</td><td>8</td><td>98</td><td>17</td><td>21</td><td>98</td><td>20</td><td>4</td><td>0</td><td>1</td><td>3</td><td>0</td><td>0</td><td>1</td><td>23</td><td>52</td></tr><tr><td>1260A-15R-3W-69-71</td><td>40</td><td>20</td><td>2</td><td>7</td><td>9</td><td>3</td><td>8</td><td>15</td><td>102</td><td>18</td><td>9</td><td>74</td><td>10</td><td>9</td><td>1</td><td>1</td><td>4</td><td>1</td><td>0</td><td>0</td><td>8</td><td>129</td></tr><tr><td>1260A-15R-4W-69-71</td><td>43</td><td>18</td><td>0</td><td>34</td><td>6</td><td>1</td><td>14</td><td>9</td><td>29</td><td>8</td><td>0</td><td>30</td><td>18</td><td>4</td><td>2</td><td>0</td><td>3</td><td>0</td><td>0</td><td>0</td><td>7</td><td>21</td></tr></table>

As we are using a high confidence value and a network that has an accuracy of about 76%, we do revise each class and remove the images from each biostratigraphic class that is misclassified, and thereafter we update the values again on the same type of table (see Table 5.3).
<figure><img src="image_76.png" /><figcaption>**Table 5.3.** Biostratigraphic important species having all the misclassified specimens removed from each class after revision. The yellow-green colour transition is the number of specimens/images recognized by the CNN, green being the highest. Gray names include species that was not originally trained by the CNN.</figcaption></figure>
5.3.2 Correlation with earlier biostratigraphic work

Our samples are retrieved from different intervals to complement for a higher resolution data.
The results from Table 5.3 along with the earlier biostratigraphic work made on Site 1260
(Meunier and Danelian, 2022), were merged (see Fig. 5.1), displaying the different species
occurences obtained from both Meunier and Danelian (2022) and from the CNN prediction in
this study. Note that the taxonomic names from both studies vary, and to avoid any confusion
see the taxonomic appendix.
<figure><img src="image_77.png" /></figure>

New ranges for this study

Ranges by Meunier and Danelian (2022)

Ranges by Meunier and Danelian (2022) not
detected by the CNN

<figure><img src="image_78.png" /></figure>
**Figure 5.1.** Biostratigraphic ranges from Meunier and Danelian (2022) correlated with the ones obtained by the CNN in this study. Only the occurrence ranges from each species and the radiolarian zones including the sub biozones obtained in Meunier and Danelian (2022).

### 5.3.3 Biomarkers for the radiolarian zonation

#### RP16

No new limits of the base of RP16 based on the First Occurrence (FO) of *Podocyrtis (Lampterium) goetheana* (Haeckel, 1887). Only one true *P. goetheana* was found 1260A-6R-3W, which is from the youngest sample included in this study. However, based on a recent study by Pinto et al., (2023) a new definition of an intermediate form between *Podocyrtis (Lampterium) chalara* Riedel and Sanfilippo, 1970 and *P. goetheana* was suggested, which also affects the base of RP16. We found the intermediate form *Podocyrtis* sp. cf. *P. goetheana* (Plate 5.1.A) as far down as 8R-5W, 70-72 cm here, although the FO of the true *P. goetheana* suggested by Meunier and Danelian (2022) was first found at sample 1260A-6R-6W, 55-57 cm.

#### RP15b

The base of the newly suggested biozone RP15b (Meunier and Danelian, 2022) is based on the Last Occurrence (LO) of *Rhopalosyringium? biauratium* (Ehrenberg, 1875). This species was rare in our samples, and we did not get a lot of *R. biauratium*. Therefore, the LO of our samples are located at sample 1260A-8R-6W, 65-67 cm; while Meunier and Danelian (2022) recognized the LO at sample 1260A-8R-3W, 54-56 cm.

#### RP15/RP15a
The next biozone is RP15 or RP15a; and the base is recognized by the Evolutionary Transition (ET) between *Podocyrtis (Lampterium) mitra* Ehrenberg, 1854 and *P. chalara*. This limit is complicated in a lot of ways, the first one is that *Podocyrtis (Lampterium) trachodes* Riedel and Sanfilippo, 1970 and *P. mitra* are defined as the same species group by the trained CNN, secondly, we are tracking the limit of where the majority of the specimens for each species are located, and many forms apart from *P. mitra* also includes *P. trachodes*, which we separated manually for comparisons here. Then there are a lot of intermediate forms, and many taxonomists have different opinions on where to draw the line between different species and intermediates. In this case, we recognized that the base of RP15a is a bit diffused, we have recognized early forms of *P. chalara* rather than late forms of *P. mitra*, which is suspected to have occurred in the earlier study. The pores were carefully counted to have a maximum circumference of 10 or 12, and more than 13 is recognized as a *P. mitra*. Our base appears lower at sample 1260A-9R-2W, 64-66 instead of 1260A-8R-6W, 54-56, if only relying on the revised data from the CNN. However, the number of images from the CNN is not that many, therefore it is not correct to determine the base on RP15a from this study. It can also be very difficult for the CNN to distinguish between having more or less than 13 pores.

### RP14b

The base of RP14b (Meunier and Danelian, 2022) is based in the FO *Sethochytris triconiscus* Haeckel, 1887. The FO in our samples is compared to the work made by Meunier and Danelian (2022) appearing a bit later at sample 10R-4W, 64-66 cm, probably due to the sample resolution, meaning that the RP14b limit remains from the FO found by Meunier and Danelian (2022) at sample 11R-2W, 55-57 cm.

### RP14/RP14a
The ET between *Podocyrtis (Lampterium) sinuosa* Ehrenberg, 1875 and *P. mitra* marks the base of RP14 or RP14a. Since we are dealing with transitional forms and *P. mitra* again, things remain complicated and rare *Podocyrtis fasciolata* (Nigrini, 1974), could be mistaken as intermediate juvenile forms between *P. sinuosa* and *P. chalara*. It is worth taking in consideration that the feet in *P. fasciolata* are underdeveloped. All specimens interpreted to be *P. fasciolata* are removed from this study.

### RP13b

The newly suggested biozone RP13b's base is defined by the LO of *Podocyrtis (Podocyrtoges) phyxis* Sanfilippo and Riedel, 1973, and since *P. phyxis* is poorly trained by the CNN, only one true specimen was recognized by the CNN, meaning that the previous limit made by Meunier and Danelian (2022) remains the same.

### RP13/RP13a

The base of RP13 or RP13a is recognized by the ET between *P. phyxis* and *P. chalara*. Since both of these species are trained poorly resulting in a very low recall number, not a lot of specimens are recognized by the CNN and therefore the previous limit made by Meunier and Danelian (2022) remains the same for now.

### 5.3.4 Remaining bio events

Most of the bio events stay the same, and Figure 5.1 can be looked at in more detail for that. However, there are some differences worth mentioning and explaining.

The LO of *Lophocyrtis alauda* (Ehrenberg, 1875) was identified by Meunier and Danelian (2022), to occur at 1260A-10R-5W, 55-57, and the LO in this study identified the LO at 1260A-
11R-3W, 61-63, where they are more or less common. However, a single large specimen resembling *L. alauda* was found at sample 1260A-6R-5W, 63-65 (see Plate 5.1.B).

*P. trachodes*, is not trained by the CNN as an individual class and is instead included in *P. mitra* group, but to make this study comparable with the one obtained by Meunier and Danelian (2022), we therefore manually divided the specimen predicted as *P. mitra* group, into either *P. trachodes* or *P. mitra*, and we based our decision mainly on the rough outline of the abdomen.

Still using a CNN saves a lot of time. Yet again, this class decision can be affected by different opinions between taxonomists. We placed the LO of *P. trachodes* at 1260A-6R-4W, 20-22, based on a single specimen (see Plate 5.1.C). *Podocyrtis mitra* is known to exist rarely around this interval.
<figure><img src="image_79.png" /><figcaption>**Plate 5.1.** A) An example of *Podocyrtis* sp. cf *P. goetheana* (Pinto et al., 2023) a typical intermediate form between *P. chalara* and *P. goetheana*. B) The single *A. alauda* specimen appearing at sample 1260A-6R-4W-119-121. C) A single specimen of *P. trachodes* appearing at sample 1260A-6R-4W, 20-22.</figcaption></figure>
**5.4 Discussion**

**5.4.1 General discussion**

Even from the fully trained CNN, one can sometimes see clusters of a larger number of identified specimens for certain species; already from there one may make approximate estimations on their appearances or where they might appear in a higher abundance. Clear examples of this can be seen within *Dictyomitra parva* (Kim, 1992), *Rhopalosyringium? auriculaleporis* (Clark and Campbell, 1942), *L. triconsiscus*, *Aphetocyrtis zamenhofi* Meunier and Danelian, 2023, *Phormocyrtis embolum* Ehrenberg, 1875, *P. chalara* and *P. goetheana*.

Some species can be more reliable than others to use for an entirely automatic classification (high recall and high accuracy), while others can be used in a semi-automatic way (high recall), with or without trained taxonomists, which are depending on whether the morphospecies are easy to recognize by anyone. Some classes might also need to be trained or regrouped before being able to be used for any kind of automatic recognition. Below we revised each species class and their reliability.

The use of automatic image recognition can also remove human biases, such as the expectation of certain taxa, not appearing at different intervals, even though they may exist rarely but are not identified as that species, since the brain ignored or categorized the specimen as unknown.

**5.4.2 Individual species revision and their credibility in future biostratigraphic work**

*A. zamenhofi*

A lot of similar looking taxa are misclassified into this class, due to the low confidence score. However, most specimens belonging to *A. zamenhofi* is recognized here, having a high recall
value, and this form is relatively easy to recognize. This class can be used for automatic image
recognition by revision of non-taxonomists.

*Carpocanopsis ornata* (Ehrenberg, 1875) group

This class was trained with a 50 % accuracy in Carlsson et al., 2023; and was not expected to be performing well due to the similarities with a lot of other Nassellarian species. However, the CNN was still able to capture a lot of *C. ornata*. This class could both be trained better and be used in a semi-automatic way, with the revision of an Eocene taxonomist.

*D. parva*

Even though this material has not been used to calculate relative abundances, we can still see
that there are a large number of *D. parva* in the older samples. There are still a lot of other
particles mistaken in this class, but this species is very easy to recognize even by a non-
taxonomist and therefore could be easily revised after automatic recognition by the CNN. In
this study we did not find any *D. parva* in 14R-5W, 65-67 cm, a range they should exist in
according to the biostratigraphic study by Meunier and Danelian (2022). One simple reason
could be that they are not so abundant there.

*Eusyringium lagena* (Ehrenberg, 1875)

Very easy species to recognize and it has both a high accuracy and recall. This species class
trained by this CNN could easily be applied for automatic image recognition without any major
revisions.

*L. vespertilio*

Easily recognizable having both a high accuracy and a high recall value and therefore excellent
to use for completely automatic recognition without the need for too much revision.
### *L. alauda*

Relatively high recall value, and easy to be recognized for a trained taxonomist. For non-taxonomists, it could be difficult to separate specimens belonging to the Lophocyrtiidae family, since this family is very diverse. Therefore, this class trained by this CNN can be used for semi-automatic classification requiring a radiolarian specialist to revise.

### *Lophophaena radians* Ehrenberg, 1875 group

*L. radians* were very rare in our samples, although many images/specimens were misclassified as *L. radians*. In the trained CNN, they have an accuracy and recall value of about 70 %, but since we use a low confidence score more images are expected to be misclassified.

### *Lychnocanium babylonis* (Clark and Campbell, 1942) group

This group of species is well trained and therefore has a high accuracy and recall and could potentially be used for automatic image recognition without any major revisions.

### *P. embolum*

*P. embolum* seems to be relatively common or abundant at the interval it exists in. It has a high recall value and is easy to recognize by a trained taxonomist.

### *Phormocyrtis lazari* Meunier and Danelian, 2023

*P. lazari* has a high recall but only exists in the younger materials. The older materials are often mistaken for *Theocyrtis scolopax* (Ehrenberg, 1875), even though they do not exist during the same interval.

### *P. chalara*

We have done no relative abundance estimations here therefore we cannot know what percentages of abundance the *P. chalara* have in relation to all radiolaria in specific samples.
However, *P. chalara* is one of the most abundant species among all these species, especially at the interval it exists. It has a high recall and accuracy and could be used for automatic classification. However, early *P. chalara* are even for taxonomist difficult to classify, but typical forms can be identified even by non-specialists.

*Podocyrtis (Lampterium) goetheana* (Haeckel, 1887)

*P. goetheana* is well-trained and has both a high accuracy and recall value. However, the samples from site 1260A, are not young enough to display the typical forms of *P. goetheana* with the very long second abdominal pore and long straight bars. This is one of the easiest radiolarian species to recognize even by a non-taxonomist.

*P. mitra* group

Not a lot of typical *P. mitra* were captured here, it can have to do with low abundance or low recall value, or their near transition from *P. sinuosa* to *P. mitra* and from *P. mitra* to *P. chalara*. Although, *P. trachodes* included in this group is always recognized.

*P. sinuosa*

This species class has a low recall value. It is known to be misclassified for *Podocyrtis papalis* Ehrenberg, 1847. Therefore, we do not obtain a lot of *P. sinuosa*.

*Podocyrtis (Podocyrtoges) ampla* Ehrenberg, 1875

*P. ampla* also has a low recall value and is often mistaken as other closely related *Podocyrtis* species which also have bad recall and accuracy values. However, the accuracy is high for *P. ampla*, as not a lot of other species are misclassified into this group.

*Podocyrtis (Podocyrtoges) diamesa* Riedel and Sanfilippo, 1970
No *P. diamesa* was recognized correctly by the CNN. Therefore, this trained CNN, is not considered being used for biostratigraphic approaches using neural networks. Not that *P. diamesa* is very similar to its neighboring species like *P. papalis* and *P. phyxis* (Carlsson et al., 2022; Carlsson et al., 2023?). After revising *P. diamesa*, not even one was found. This could be partly because of two reasons; one is that *P. diamesa* is not very abundant within the studied material or it has a very low recall value.

*P. phyxis*

As *P. sinuosa*, *P. ampla* and *P. diamesa*; *P. phyxis* has a low recall value, only resulting in one specimen being identified here. This class also has a low accuracy since species like *P. papalis*, *P. diamesa* and *P. ampla* are more or less mistaken as *P. phyxis*.

*R? auriculaleporis*

This class has a high recall, most *R? auriculaleporis* is recognized in this class along with other classes, such as *Dictyomitra* Zittel 1876 species. It can be used for semi-automatic identification with some revision by both taxonomists and non-taxonomists.

*R? biauritum*

This class is rare in our samples, and it looks like *R? auriculaleporis*, just having fewer and more sparsely separated pores. They still have a high recall value and could be used for automatic image classification with some revisions made even for non-taxonomists.

*Sethochytris triconiscus* Haeckel, 1887

Easily recognizable, having both a high accuracy and recall value. Therefore, it can be used for automatic image recognition by anyone.

*T. scolopax*
Conversely to *P. lazari*, *T. scolopax* has a little lower recall value since some of its specimens
are misclassified as *P. lazari*. However, not a lot of *P. lazari* are recognized as *T scolopax*.

*Zeolithapium mitra* (Ehrenberg, 1875)

Despite using a low or high confidence score, a lot of images are misclassified to this class
making it difficult to use for automatic image recognition. Even if this class has a high recall
value, one must delete numerous of images that are not *Z. mitra*.

5.4.3 Time and Effort

While training a neural network requires substantial data and significant effort for supervised
labeling, this challenge becomes particularly pronounced in the case of radiolarians with close
similarities to others, and this dataset, in particular, has too many morphologically similar
species.

The process of imaging, on the other hand, is largely automated, requiring minimal manual
intervention. However, it is worth noting that accumulating a substantial number of images,
around 90,000 images from 23 samples in this study is a bit time-consuming. One sample
including 8 coverslips, would take approximately 10 hours, including the time of setting the
microscope and saving images. Nevertheless, once you have gathered all the images, the
analysis process is fast and consistent if you already have a trained CNN.

5.5 Conclusion

This work concludes that many of these species trained by a CNN can be applied for real
biostratigraphic work and interpretations.

Just consider that the sediment slides are prepared in a way to avoid touching each other and
forming lumps. Therefore, not a lot of sediment was used. This makes it possible for the CNN
to sometimes not detect rarely occurring species at certain intervals. Some species are better
trained than others because they have both a high recall and a high accuracy and could be
completely used for automatic identification by non-taxonomists. Other species could be used
for automatic recognition with some revisions, while some classes need to be better trained,
especially those with a low recall value.

Here, we were sometimes able to expand the biostratigraphic ranges for some classes, while in
other cases we were not able to detect certain species intervals due to either poorly trained or
the unavailability to capture rare occurring species from preparing the sediment slides.

## Acknowledgments

This study has been partly funded by the French government through the program
“Investissements d'avenir” (I-ULNE SITE/ANR-16-IDEX-0004 ULNE) managed by the
National Research Agency. Funding from the European Union's Horizon 2020 research and
innovation program under the Marie Sklodowska-Curie grant agreement no. 847568 was also
received and it was also supported by UMR 8198 Evo-Eco-Paléo and IRCICA (CNRS and
Univ. Lille USR-3380).

Many thanks to Sylvie Regnier for helping to prepare samples by removal of non-siliceous
material.
# CHAPTER 6: General Conclusions and Perspectives

## 6.1 General conclusions

This thesis has aimed to attempt to simplify or automate Eocene radiolarian micropaleontological work by using artificial neural networks for automatic image recognition; is here presented how each chapter answers the following questions, which were presented at the beginning of this thesis:

Q.1. How can neural network learning achieve equal accuracy in the identification of Middle Eocene radiolarian species as an Eocene specialist in radiolarian taxonomy?

Q.2. How can machine learning techniques be compared to classical morphometric analysis?

Q.3. How can Spiking Neural Networks (SNNs) be compared with other deep learning methods?

Q.4 Can a trained neural network on a whole middle Eocene tropical radiolarian assemblage be directly applied for example to biostratigraphic or palaeoceanographic studies?

The second chapter focused on the training of biostratigraphically significant species within the genus *Podocyrtis*. In this study, we exclusively used specimens that belonged to clearly defined specific species, carefully excluding any intermediate or uncertain forms. The training accuracy achieved with MobileNet v.1 reached approximately 91%. We systematically categorized images into three distinct groups: one for unbroken and clear images; another for images with only broken parts removed; and a third group that retained all variations. We tested this approach with newly acquired samples from different locations, yielding accuracy rates ranging from 59% to 77%, depending on the specific dataset used. Nevertheless, it is worth noting that
in the majority of cases, when specimens were not assigned to their true class, specimens were mistakenly classified to at least a closely related species within the same evolutionary lineage.

If a neural network has undergone comprehensive training on morphologically distinct species, there is a strong likelihood that it can achieve accuracy levels comparable to those of an experienced middle Eocene radiolarian taxonomist. One notable advantage of a well-trained neural network is its ability to quickly analyse thousands of images almost instantaneously. Moreover, it can effectively differentiate between closely related and visually similar forms, provided that all such forms have been sufficiently trained. Using a pre-trained CNN for biostratigraphic applications is not only efficient but also enjoyable, as the machine learning model takes charge of specimen identification and counting for each interval. The images just have to be verified how they were categorized into different classes. Nonetheless, it is worth noting that the image acquisition process, even when using an automated microscope, can be time-consuming when dealing with a substantial size of samples.

In a follow-up study conducted by a Master 2 student that I supervised (Francisco Pinto) we dealt with the question of intermediate forms. Our case study focused on intermediate forms between the species *Podocyrtis chalara* and *Podocyrtis goetheana*, representing the end members of the *Lampterium* lineage. We identified two distinct types of intermediate forms. These intermediates, alongside *P. goetheana* and *P. chalara*, underwent attempted identification using various machine learning algorithms based on neural networks, which directly processed image data as input. This approach was compared to Linear Discriminant Analysis (LDA), which utilizes morphometric data for identification purposes. Both LDA and neural networks were able to recognize the intermediate form that is close to *P. goetheana*
(*Podocyrtis* sp. cf. *P. goetheana*). However, both methods were also unable to separate the intermediate form that is nearest to *P. chalara*, *Podocyrtis* sp. cf. *chalara*, since the neural network often misclassified it as *P. chalara*, and the LDA showed that *P. chalara* and *Podocyrtis* sp. cf. *chalara* both share the same morphospace.

By trying to address the second question (Q.2) we achieved similar results when comparing machine learning techniques to classical morphometric analyses, even in the case of distinguishing intermediate forms between two species. Although LDA yielded slightly higher accuracy, the overall outcome remained consistent. The key distinction lies in the methodology. Traditional morphological approaches demand manual measurement of specific values for each individual specimen. In contrast, neural networks simply require the input of images, but to ensure their efficiency, a substantial number of images representing each unique individual, including variations in, for example, rotation is essential. Fortunately, this can be easily obtained using an automated microscope.

To continue on this third chapter, the neural networks used were both traditional CNNs and SNNs. Spiking neural networks are not commonly used for image recognition as widely as CNNs; they are known to perform less well when it comes to accuracy for image recognition, but in recent years they have improved their performance. Apart from being energy efficient and memory saving, SNNs closely imitate natural neural networks and are more suitable to process spatio-temporal data. In this chapter, we could see that using STDP-SNN Network gave a near result of what the trained VGG16 CNN architecture obtained. However, the use of a SuperSpike-SNN Network was less successful, part of it could be explained that they used fewer layers than VGG16, and generally have a smaller network.
To answer the third question (Q.3), there are some SNNs like the STDP-Network, that can give nearly as accurate results as a CNN. The STDP-Network also have a training speed of about 5-6 minutes and VGG16 had in comparison to that a training speed of about 17-18 minutes while VGG16 only performed better by approximately 2%.

In the fourth chapter, we tackled the comprehensive analysis of radiolarians and other particles, resulting in a substantial dataset containing 12,217 images divided into 96 trained classes, effectively representing a wide range of objects encountered. We achieved an overall training accuracy of approximately 86% and a testing accuracy of about 76%. Within these 96 classes, 39 were categorized at the species level, including biostratigraphically important and frequently encountered species. Our primary objective was to closely examine these species to explore potential applications, which would also provide us with an answer to the last (Q.4) question. Our findings revealed that many of these species could indeed serve in biostratigraphic analyses, where the presence or absence of a class is the key requirement. However, our ultimate goal was to employ these species for relative abundance studies. Unfortunately, we encountered a significant barrier, as a considerable number of specimens appeared in different levels of broken forms and some images consisted of particles that were occasionally lumped together with several specimens, making it difficult to count the exact number of radiolaria. Also, some species or classes exhibited low recall values, indicating a risk of misclassification into multiple other classes. Consequently, these classes were not considered optimal for use in relative abundance studies.

The fifth chapter extends the discussions from the fourth chapter, exploring the practical application of a trained CNN in a biostratigraphic context. We draw parallels with an earlier study conducted on the same Site 1260, essentially building upon prior research. Our findings
in this chapter largely confirmed those of the earlier study, revealing the consistent presence of
similar species within specific intervals. However, there were instances where the trained CNN
helped extend the biostratigraphic ranges and offered a more comprehensive perspective.
Conversely, in cases where certain species were less frequent, the CNN was less effective in
detecting their presence within intervals where they should theoretically exist.

## 6.2 Perspectives

Not only is the focus of the neural networks themselves important but data collection and preparation play a huge part in this, as well. Suppose we start from the beginning with the sample preparation. In that case, it is very important to do this step properly to retrieve the best samples, free from any breakage, or without having unwanted elements like organic or calcareous fossil remains present, and to carefully dissolve and remove clay particles and lumped sediments using an ultrasonic bath.

When preparing slides, striking the right balance is crucial. It is essential to ensure that the sediment is sufficiently distributed to prevent excessive clumping, yet not so sparse that it becomes inefficient when photographing under an automatic microscope also missing rare existing radiolarian species. Currently, we use 1.2 x 1.2 cm slides primarily because the automatic microscope we use generates composite images that are too large for our segmentation software to handle directly and they have to be converted into grayscale to fit the segmentation tool. Producing larger images with lower resolution is not the optimal solution, as it compromises image quality for the sake of accommodating larger images.

A more favourable solution would be the accessibility to more robust softwares capable of
handling larger images. Additionally, extending the duration of the automatic microscope's
operation to capture a larger area, which doesn't require hourly monitoring, would be a more
efficient approach.

Another critical consideration affects the choice of mounting media, as it significantly impacts
image contrast, each with its own advantages and weaknesses. When it comes to automatic
image recognition, opting for a mounting medium that yields well-contrasted images is ideal.
Examples of such media include Canada Balsam (Fig. 6.1A) and Norland epoxy (Fig. 6.1B). In
this study, Norland epoxy has been the preferred choice due to its simplicity, rapid drying with
a UV lamp, and its ability to produce high-contrast images. However, one limitation of this
product is the occasional presence of trapped bubbles and bubble inclusions within radiolarian
specimens, which can obviously affect the results of neural networks.

On the other hand, Canada Balsam (Fig. 6.1A) offers the highest image quality, but it has other
drawbacks such as slow settlement, an elevated price, and a tendency to become yellow and
fade with age (Ravikumar et al., 2019). In contrast, the Eukitt mounting media (Fig. 6.1C) fails
to deliver well-contrasted images, although it avoids the issue of bubble inclusions. However,
it is not recommended for photographic purposes due to its limitations in contrast enhancement.

<figure><img src="image_80.png" /><figcaption>**Figure 6.1.** Radiolarian images A) with Canada Balsam as mounting media, B) with Norland epoxy as monutning media, and C) with Eukitt as mounting media.</figcaption></figure>
In addition to improving neural networks, the creation of robust datasets is equally important. When making these datasets, it is essential to acknowledge that subtle distinctions often differentiate one species from another, and several species may bear an outstanding resemblance. In such cases, it may be necessary to group various similar-looking species, especially when their taxonomic affinity remains uncertain. Conversely, it becomes critically important to collect a substantial number of images for closely resembling species, enabling the neural network to eventually distinguish between these classes.

Furthermore, if neither human experts nor any morphometric approaches can reliably differentiate between two distinct species or face challenges in doing so, it is reasonable to expect the neural network to encounter similar difficulties. Clarity is the key when determining the images assigned to each class; the inclusion of incorrect or uncertain specimens in different classes must be avoided. It's often more practical to initially exclude uncertain specimens, with the possibility of later dividing them into distinct groups as more images are collected and clear distinguishing characteristics emerge.

Additionally, it is preferable to minimize the focus on non-radiolarian elements or other unnecessary particles. Broken fragments, bubbles, and other particles are very common, or even more common than the radiolaria themselves. Overutilizing such data in datasets can lead to overtraining, where the neural network's classification is dominated by these non-relevant features. Hence, careful consideration should be given to have a balance in the dataset composition to ensure effective model training.

One crucial aspect to keep in mind is the incomplete and sometimes unreliable radiolarian taxonomy. Radiolarians can be grouped differently based on their resemblance to taxa that are
not closely related, thereby posing a challenge in their taxonomic classification. Despite
extensive efforts to organize radiolarians through morphology and morpho-molecular
approaches, the reliability of taxonomy remains questionable, except at the species level.
Furthermore, there exists a substantial number of undescribed radiolarian species, and many
forms are difficult to describe. These forms often bear strong resemblances to multiple other
species and may exist as intermediate forms linking the gap between some established species.
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# Appendices
Tropical Atlantic middle
Eocene Radiolaria
catalogue from the
Demerara Rise, ODP Leg
207

Radiolarians here are used to train CNNs for
automatic image classification and have been
balanced to both fit an acceptable taxonomic
framework as well as being adjusted to be trained
with a high accuracy.
List of all classes

ACANTHODESMIOIDEA ACANTHODESMIIDAE *Eucoronis hertwigi* group

ACANTHODESMIOIDEA CEPHALOSPYRIDIDAE cephalospyridid group A

ACANTHODESMIOIDEA CEPHALOSPYRIDIDAE cephalospyridid group B

ACANTHODESMIOIDEA CEPHALOSPYRIDIDAE cephalospyridid group C

ACANTHODESMIOIDEA CEPHALOSPYRIDIDAE cephalospyridid group D

ACANTHODESMIOIDEA CEPHALOSPYRIDIDAE *Dendrospyris stylophora*

ACANTHODESMIOIDEA CEPHALOSPYRIDIDAE *Elaphospyris didiceros* group

ACANTHODESMIOIDEA CEPHALOSPYRIDIDAE Unknown cephalospyridid group

ACANTHODESMIOIDEA CEPHALOSPYRIDIDAE *Liriospyris clathrata* group

ACANTHODESMIOIDEA CEPHALOSPYRIDIDAE *Petalospyris anthocyrtoides* group

ACANTHODESMIOIDEA juvenil or broken Acanthodesmioidea

ACANTHODESMIOIDEA Smooth surfaced Acanthodesmioidea

ACANTHODESMIOIDEA Spinose Acanthodesmioidea

ACANTHODESMIOIDEA STEPHANIIDAE *Zygocircus* spp

AMPHIPYNDACOIDEA AMPHIPYNDACIDAE *Amphipternis* cf. *clava*

ARCHAEODICTYOMITROIDEA ARCHAEDICTYOMITRIDAE *Dictyomitra parva*

ARTOSTROBIOIDEA ARTOSTROBIIDAE artostrobiid group

ARTOSTROBIOIDEA ARTOSTROBIIDAE *Artostrobus quadriporus*

ARTOSTROBIOIDEA ARTOSTROBIIDAE *Dictyoprora amphora* group

ARTOSTROBIOIDEA ARTOSTROBIIDAE *Dictyoprora mongolfieri*

ARTOSTROBIOIDEA ARTOSTROBIIDAE *Dictyoprora* spp

ARTOSTROBIOIDEA ARTOSTROBIIDAE *Siphocampe* spp

ARTOSTROBIOIDEA ARTOSTROBIIDAE *Siphocample pupa* group

ARTOSTROBIOIDEA ARTOSTROBIIDAE *Tricolapsa* spp

ARTOSTROBIOIDEA RHOPALOSYRINGIIDAE *Rhopalosyringium auriculaleporis*

ARTOSTROBIOIDEA RHOPALOSYRINGIIDAE *Rhopalosyringium biauritum*

Background blurry

Broken

Broken diatoms

CARPOCANIOIDEA CARPOCANIIDAE *Carpocanopsis ornata* group

CYCLADOPHOROIDEA CYCLADOPHORIDAE *Cycladophora spatiosa* group

Diatoms

Double

Edges

EUCYRTIDIOIDEA EUCYRTIDIIDAE *Eucyrtidium* spp

EUCYRTIDIOIDEA EUCYRTIDIIDAE *Stichopterygium microporum*

EUCYRTIDIOIDEA EUCYRTIDIIDAE *Stichopterygium picus*

Foraminifera

HELIOSATURNALOIDEA AXOPRUNIDAE *Axoprunum* sp A

LITHELIOIDEA SPONGURIDAE *Middourium* group

LITHOCHYTRIDOIDEA BEKOMIDAE *Dictyophimus craticula*

LITHOCHYTRIDOIDEA LITHOCHYTRIDIDAE lithochytridid group

LITHOCHYTRIDOIDEA LITHOCHYTRIDIDAE *Lithochytris vespertilio*

LITHOCHYTRIDOIDEA LITHOCHYTRIDIDAE *Lychnocanium babylonis* group

LITHOCHYTRIDOIDEA LITHOCHYTRIDIDAE *Lychnocanoma bajunensis*

LITHOCHYTRIDOIDEA LITHOCHYTRIDIDAE *Sethochytris triconiscus*

LITHOCYCLIOIDEA LITHOCYCLIIDAE *Lithocyclia ocellus*

LITHOCYCLIOIDEA PHACODISCIDAE *Periphaena decora*

NASSELLARIA group A

NASSELLARIA group B

NASSELLARIA group C

Particles

PLAGIACANTHOIDEA CERATOCYRTIDAE *Ceratocyrtis* spp

PLAGIACANTHOIDEA DIMELISSIDAE *Lithomelissa ehrenbergii* group

PLAGIACANTHOIDEA DIMELISSIDAE *Lithomelissa macroptera*

PLAGIACANTHOIDEA group A

PLAGIACANTHOIDEA group B

PLAGIACANTHOIDEA group C

PLAGIACANTHOIDEA LOPHOPHAENIDAE *Lophophaena radians* group

PLAGIACANTHOIDEA LOPHOPHANIDAE *cf* *Lophophaena simplex*

PLAGIACANTHOIDEA LOPHOPHANIDAE *Lophophaena capito* group

PLAGIACANTHOIDEA LOPHOPHANIDAE *Lophophaena spp*
PLAGIACANTHOIDEA PLAGIACANTHIDAE *Rhabdolithis pipa*

PLECTOPYRAMIOIDEA *Incertae familiae Zeolithapium mitra*

PLECTOPYRAMIOIDEA *Incertae familiae Zeolithapium* spp

PLECTOPYRAMIOIDEA PLECTOPYRAMIDIDAE plectopyramidid group

PTEROCORYTHOIDEA LOPHOCYRTIIDAE *Aphetocyrtis zamenhofi*

PTEROCORYTHOIDEA LOPHOCYRTIIDAE *Apoplanius* spp

PTEROCORYTHOIDEA LOPHOCYRTIIDAE lophocyrtiid group A

PTEROCORYTHOIDEA LOPHOCYRTIIDAE lophocyrtiid group B

PTEROCORYTHOIDEA LOPHOCYRTIIDAE *Lophocyrtis alauda*

PTEROCORYTHOIDEA LOPHOCYRTIIDAE *Lophocyrtis barbadense*

PTEROCORYTHOIDEA PTEROCORYTHIDAE *Albatrossidium* spp

PTEROCORYTHOIDEA PTEROCORYTHIDAE *Calocyclas hispida*

PTEROCORYTHOIDEA PTEROCORYTHIDAE *Calocycloma ampulla*

PTEROCORYTHOIDEA PTEROCORYTHIDAE *Phormocyrtis embolum*

PTEROCORYTHOIDEA PTEROCORYTHIDAE *Phormocyrtis lazari*

PTEROCORYTHOIDEA PTEROCORYTHIDAE *Podocyrtis Lampterium chalara*

PTEROCORYTHOIDEA PTEROCORYTHIDAE *Podocyrtis Lampterium goetheana*

PTEROCORYTHOIDEA PTEROCORYTHIDAE *Podocyrtis Lampterium mitra* group

PTEROCORYTHOIDEA PTEROCORYTHIDAE *Podocyrtis Lampterium puellasinensis*

PTEROCORYTHOIDEA PTEROCORYTHIDAE *Podocyrtis Lampterium sinuosa*

PTEROCORYTHOIDEA PTEROCORYTHIDAE *Podocyrtis Podocyrtis papalis*

PTEROCORYTHOIDEA PTEROCORYTHIDAE *Podocyrtis Podocyrtoges ampla*

PTEROCORYTHOIDEA PTEROCORYTHIDAE *Podocyrtis Podocyrtoges diamesa*

PTEROCORYTHOIDEA PTEROCORYTHIDAE *Podocyrtis Podocyrtoges phyxis*

PTEROCORYTHOIDEA PTEROCORYTHIDAE *Podocyrtis Podocyrtopsis apeza*

PTEROCORYTHOIDEA PTEROCORYTHIDAE *pterocorythid* group

PTEROCORYTHOIDEA PTEROCORYTHIDAE *Theocyrtis scolopax*

PTEROCORYTHOIDEA PTEROCORYTHIDAE *Theocyrtis* spp

PTEROCORYTHOIDEA THEOCOTYLIDAE *Pterocodon* *apis*

PTEROCORYTHOIDEA THEOCOTYLIDAE *Theocorys* spp

PTEROCORYTHOIDEA THEOCOTYLIDAE *Theocotyle* spp

PTEROCORYTHOIDEA THEOCOTYLIDAE *Thyrsocyrtis Pentalocorys krooni*

PTEROCORYTHOIDEA THEOCOTYLIDAE *Thyrsocyrtis Pentalocorys triacantha*

PTEROCORYTHOIDEA THEOCOTYLIDAE *Thyrsocyrtis Thyrsocyrtis rhizodon*

PTEROCORYTHOIDEA THEOPERIDAE *Eusyringium fistuligerum* group

PTEROCORYTHOIDEA THEOPERIDAE *Eusyringium lagena*

PTEROCORYTHOIDEA THEOPERIDAE *Rhopalocanium ornatum* group

PTEROCORYTHOIDEA THEOPERIDAE *Rhopalocanium sphinx*

PYLOBOTRYDOIDEA PYLOBOTRYDIDAE pylobotrydid group

Spicules

SPUMELLARIA Discoidal biconvex spumellarians with equatorial spines

SPUMELLARIA Discoidal spongy spumellarians

SPUMELLARIA Discoidal spongy spumellarians with rod shaped spines

SPUMELLARIA Discoidal spumellarians with irregular concentric rings

SPUMELLARIA Discoidal spumellarians with regular concentric rings

SPUMELLARIA Multi armed spongy spumellarians

SPUMELLARIA Small discoidal spumellarians with concentric rings

SPUMELLARIA Spherical spumellarians with large pores

SPUMELLARIA Spherical spumellarians with radial spines

SPUMELLARIA Two armed spongy spumellarians

STICHOPILIOIDEA STICHOPILIIDAE *Lophoconus antilope*

STYLOSPHAEROIDEA STYLOPHAERIDAE *Spongatractus pachystylus*

STYLOSPHAEROIDEA STYLOSPHAERIDAE *Stylosphaera coronata* group

THEOPILIOIDEA ANTHOCYRTIDIDAE *Anthocyrtis mespilus* group
Nassellarians
Superfamily **ARCHAEODICTYOMITROIDEA** Pessagno, 1976

Family **Archaeodictyomitridae** Pessagno, 1976

Genus *Dictyomitra* Zittel, 1876

<figure><img src="image_81.png" /><figcaption>*Dictyomitra parva* (Kim, 1992)</figcaption></figure>

Superfamily **AMPHIPYNDACOIDEA** Riedel, 1967

Family **Amphipyndacidae** Riedel, 1967

Genus *Amphipternis* Foreman, 1973

<figure><img src="image_82.png" /></figure>

*Amphipternis cf. clava* (Ehrenberg, 1874)

This species differs from *Amphipternis clava* (Ehrenberg) in having more than two post-abdominal segments, numerous closely spaced pores arranged in transverse row, and in being more inflated distally.

*Not trained by the CNN*
Synonymy list

*Dictyomitra parva* (Kim)

*Eucyrtidium parva* Kim, 1992, p. 43, pl. 2, figs. 7, 8.

Theoperid gen. et sp. indet. Johnson, 1974, pl. 3, fig. 12.

*Siphocampe elizabethae* (Clark and Campbell): Nigrini, 1977, p. 256, pl. 3, fig. 6.

*Archaeodictyomitra* ? sp. Takemura, 1992, p. 744, pl. 3, figs. 1, 2.

*Dictyomitra amygdala* Shilov, 1995, p. 126, pl. 1, fig. 4–6b.

*Dictyoprora* ? *amygdala* (Shilov): Suzuki et al., 2009, p. 263, pl. 18, fig. 3.

*Siphocampe* ? *amygdala* (Shilov): Hollis et al., 2020, pl. 8, figs. 11, 12; Meunier and Danelian, 2022, p. 22, pl. 1, fig. 1.

*Amphipternis cf. clava* Ehrenberg, 1874

cf. *Lithocampe* ? *clava* Ehrenberg, 1874, p. 238; 1876, p. 76, pl. 4, fig. 3; Ogane et al., 2009, pl. 22, figs. 2a–2c.

*Amphipternis clava* (Ehrenberg): Foreman, 1973, p. 430, pl. 7, figs. 16, 17; pl. 9, fig. 2.
Superfamily **EUCYRTIDIOIDEA** Ehrenberg, 1846

Family **Eucyrtidiidae** Ehrenberg, 1846

Genus *Eucyrtidium* Ehrenberg, 1847

<figure><img src="image_83.png" /><figcaption>*Eucyrtidium levisaltarix*</figcaption></figure>

**Eucyrtidium** spp.

We included here *Eucyrtidium levisaltarix* (Meunier and Danelian, 2023), *Eucyrtidium montiparum* Ehrenberg, 1874 and other *Eucyrtidium* species with more than two post-abdominal segments, absence of feet or lateral horns and a cylindrical test outline.
<figure><img src="image_84.png" /></figure>

*Stichopterygium ? microporum* (Ehrenberg, 1874)

<figure><img src="image_85.png" /></figure>

*Stichopterygium ? picus* (Ehrenberg, 1874)

*Not trained by the CNN*
Synonymy list

*Stichopterygium ? microporum* (Ehrenberg)

*Eucyrtidium microporum* Ehrenberg, 1874, p. 230; 1876, p. 72, pl. 11, fig. 20; Ogane et al., 2009, pl. 6, figs. 5a–5c, pl. 85, figs. 5a–5f.

*Stichopodium ? microporum* (Ehrenberg): Petrushevskaya and Kozlova, 1972, p. 548, pl. 25, figs. 4–6; Funakawa et al., 2006, p. 37, pl. P13, figs. 3a–4b.

*Stichopterygium ? picus* (Ehrenberg)

*Eucyrtidium picus* Ehrenberg, 1874, p. 232; 1876, p. 72, pl. 11, fig. 1; Ogane et al., 2009, pl. 86, figs. 6a–6g.

Artostrobiid gen. et sp. indet. Riedel and Sanfilippo, 1977, pl. 9, fig. 16.
Superfamily PLECTOPYRAMIDOIDEA Haecker, 1908

Family Plectopyramididae Haecker, 1908

<figure><img src="image_86.png" /></figure>

Plectopyramidid group

Elongated conical two segmented plectopyramidids that are wide open distally and display well-arranged pore rows on the test. In some species the pores are almost square, in others they are oval, or circular to subcircular in outline. Many species have a very tiny spherical cephalis that appears poreless and bears sometimes a horn that may be tiny or large tubular or thick conical.
<figure><img src="image_87.png" /><figcaption>*Zealithapium mitra* (Ehrenberg, 1874)</figcaption></figure>

<figure><img src="image_88.png" /></figure>

*Zealithapium spp.*

Included in this class are *Zealithapium plegmacantha* (Riedel and Sanfilippo, 1970) and *Zealithapium anoectum* (Riedel and Sanfilippo, 1970).
Synonymy list

*Zealithapium mitra* (Ehrenberg)

*Cornutella mitra* Ehrenberg, 1874, p. 221; 1876, p. 68, pl. 2, figs. 8.

*Lithapium *?* mitra* (Ehrenberg): Riedel and Sanfilippo, 1970, p. 520, pl. 4, figs. 6, 7.*

*Zealithapium mitra* (Ehrenberg): O’Connor, 1999, p. 5.
Superfamily **CARPOCANIOIDEA** Haeckel, 1882

Family **Carpocaniidae** Haeckel, 1882

Genus *Carpocanopsis* Riedel and Sanfilippo, 1971

<figure><img src="image_89.png" /><figcaption>*Carpocanopsis ornata* (Ehrenberg, 1874) group</figcaption></figure>

50 µm

Superfamily **ARTOSTROBIOIDEA** Riedel, 1967

Family **Artostrobiidae** Riedel, 1967

Genus *Artostrobus* Petrushevskaya, 1967

<figure><img src="image_90.png" /><figcaption>*Artostrobus quadriporus* Bjørklund, 1976</figcaption></figure>

50 µm

Not trained by the CNN
Synonymy list

*Carpocanopsis ornata* (Ehrenberg) group

*Cryptoprora ornata* Ehrenberg, 1874, p. 222; 1876, p. 68, pl. 5, fig. 8; Ogane et al., 2009, pl. 6, figs. 2a-2c, pl. 83, figs. 5a-6d.

*Cryptocarpium ornatum* (Ehrenberg): Sanfilippo and Riedel, 1992, p. 6, 36, pl. 2, figs. 18-20.

**Remarks:** The great morphological disparity observed in *Carpocanopsis ornata* (Ehrenberg, 1874) leads us to consider it as a group of closely related species.

*Artostrobus quadriporus* Bjørklund

*Artostrobus quadriporus* Bjørklund, 1976, p. 1125, pl. 23, figs. 15-21; Hull, 1996, p. 137, pl. 4, fig. 12.

non ? *Artostrobus quadriporus* Lazarus and Pallant, 1989, p. 362, pl. 5, figs. 16, 17.
<figure><img src="image_91.png" /><figcaption>*Dictyoprora mongolfieri* (Ehrenberg, 1854)</figcaption></figure>

50 µm

<figure><img src="image_92.png" /><figcaption>*Dictyoprora amphora* (Haeckel, 1887) **group**</figcaption></figure>

50 µm

*Dictyoprora* with several tightly packed pores. In some specimens the test surface is rather rough and the pore size increases at the distal end of the thorax. Pores are quincuncially arranged, not strictly arranged in transverse and longitudinal rows like in *D. mongolfieri* (Ehrenberg, 1854).

<figure><img src="image_93.png" /><figcaption>*Dictyoprora* spp.</figcaption></figure>

50 µm

All *Dictyoprora* specimens that were uncertain to belong to either *D. mongolfieri* or *D. amphora* group were placed here. In this group there are also many other species such as *Dictyoprora ovata* (Haeckel, 1887), *Dictyoprora pirum* (Ehrenberg, 1874), *Dictyoprora curta* (Clark and Campbell, 1942), *Dictyoprora urceolus* Haeckel, 1887 and many other unknown forms.
Synonymy list

*Dictyoprora mongolfieri* (Ehrenberg)

*Eucyrtidium mongolfieri* Ehrenberg, 1854, pl. 36, fig. 18; 1874, p. 230; 1876, p. 72, pl. 10, fig. 3.

*Dictyoprora mongolfieri* (Ehrenberg): Nigrini, 1977, p. 250, pl. 4, fig. 7; Funakawa et al., 2006, p. 17, pl. P2, figs. 5a–6b.

*Dictyoprora amphora* (Haeckel) group

*Dictyocephalus amphora* Haeckel, 1887, p. 1305, pl. 62, fig. 4.

*Theocampe amphora* (Haeckel) group: Foreman, 1973, p. 431, pl. 8, figs. 7, 9–13.

*Dictyoprora amphora* (Haeckel) group: Nigrini, 1977, p. 250, pl. 4, figs. 1, 2; Funakawa et al., 2006, p. 16, 17, pl. P2, figs. 1a–2b.
<figure><img src="image_94.png" /></figure>

**Siphocampe pupa** (Ehrenberg, 1874) **group**

Two segmented artostrobiids with a large thorax and small horizontally arranged tightly packed pores.

<figure><img src="image_95.png" /><figcaption>Siphocampe pollen</figcaption></figure>

<figure><img src="image_96.png" /><figcaption>Siphocampe pachyderma</figcaption></figure>

<figure><img src="image_97.png" /><figcaption>Siphocampe acephala</figcaption></figure>

**Siphocampe spp.**

We included in this group three species, *Siphocampe pollen* Meunier and Danelian, 2023; *Siphocampe pachyderma* (Ehrenberg, 1874), and *Siphocampe acephala* (Ehrenberg, 1874).
Synonymy list

***Siphocampe pupa*** (Ehrenberg, 1861) ***group***

*Eucyrtidium pupa* Ehrenberg 1861, p. 768; 1873a, p. 311; 1873b, pl. 7, fig. 16; Suzuki et al., 2009, pl. 55, figs. 8a-8c.

**Remarks:** The great morphological disparity observed in *Siphocampe pupa* (Ehrenberg, 1874) leads us to consider it as a group of closely related species.
<figure><img src="image_98.png" /></figure>

**"Tricolocapsa" spp.**

Two segmented form often bearing a short conical horn. The cephalis is much smaller than the thorax, which seems inflated. Some specimens bear feet; it is doubtful whether all of the specimens here actually belong to the family Artostrobiidae.

<figure><img src="image_99.png" /></figure>

**Artostrobiid group**

A lump group of artostrobiids with several segments and a curvy outline belonging to the genera *Botryostrobus* and *Siphocampe*.

*Not trained by the CNN*
<figure><img src="image_100.png" /></figure>

*Rhopalosyringium ? auriculaleporis* (Clark and Campbell, 1942)

<figure><img src="image_101.png" /></figure>

*Rhopalosyringium ? biauritum* (Ehrenberg, 1874)
Synonymy list

*Rhopalosyringium ? auriculaleporis (Clark and Campbell)*

*Lophophaena* (*Lophophaenula*) *auriculaleporis* Clark and Campbell, 1942: p. 76; pl. 8, figs. 20, 27–29; Blueford, 1988, p. 246, pl. 3, figs. 1–3.

*Artobotrys auriculaleporis* (Clark and Campbell): Petrushevskaya and Kozlova, 1979, p. 137, fig. 515; Meunier and Danelian, 2022, p. 18, pl. 1, fig. 7.

*Rhopalosyringium ? biauritum (Ehrenberg)*

*Eucyrtidium biauritum* Ehrenberg, 1874, p. 226; 1876, p. 70, pl. 10, figs. 7, 8; Ogane et al., 2009, pl. 18, figs. 8a–d, pl. 20, figs. 1a–2b, 6.

*Eucyrtidium bicorne* Ehrenberg, 1874, p. 226; 1876, p. 70, pl. 11, fig. 7; Ogane et al., 2009, pl. 20, figs. 3a, 3b, 4a–4c, 5a–5c.

*Lophocyrtis biaurita* (Ehrenberg): Haeckel, 1887, p. 1411; Riedel and Sanfilippo, 1978, p. 70, pl. 6, fig. 13.

*Artobotrys biaurita* (Ehrenberg): Petrushevskaya and Kozlova, 1979, p. 136; Meunier and Danelian, 2022, p. 18, pl. 1, fig. 6.
Superfamily **ACANTHODESMIOIDEA** Haeckel, 1862

Family **Acanthodesmiidae** Haeckel, 1882

Genus *Eucoronis* Haeckel, 1882

<figure><img src="image_102.png" /><figcaption>*Eucoronis hertwigi* (Bütschli, 1882) group</figcaption></figure>

Not trained by the CNN
Synonymy list

*Eucoronis hertwigi* (Bütschli) group

*Acanthodesmia hertwigi* Bütschli, 1882, pl. 32, fig. 9a-9c.

*Eucoronis hertwigii* (Bütschli) group: Petrushevskaya and Kozlova 1972, p. 533, pl. 41, figs. 15-17; Kamikuri, 2015, pl. 13, fig. 18.
Family **Cephalospyrididae** Haeckel, 1882

Genus *Dendropyris* Haeckel, 1882

<figure><img src="image_103.png" /><figcaption>*Dendropyris stylophora* (Ehrenberg, 1874)</figcaption></figure>

Genus *Elaphospyris* Haeckel, 1882

<figure><img src="image_104.png" /></figure>

*Elaphospyris ? didiceros* (Ehrenberg, 1874) **group**

We place here all cephalospyridids with one apical horn, two antapical horns diverging laterally at ca. 45° and variable number of feet.
Synonymy list

*Dendropyris stylophora* (Ehrenberg)

*Ceratospyris stylophora* Ehrenberg, 1874, p. 220; 1876, p. 66, pl. 20, fig. 10; Ogane et al., 2009, pl. 38, fig. 6a–6c, pl. 39, figs. 6a, 6b.

*Dendropyris stylophora* (Ehrenberg): Goll, 1968, p. 1423, pl. 173, figs. 21–24, text–fig. 8.

*Elaphospyris ? didiceros* (Ehrenberg) group

*Ceratospyris didiceros* Ehrenberg, 1874, p. 218; 1876, p. 66, pl. 21, fig. 6; Ogane et al., 2009, pl. 39, figs. 1a–1c.

*Giraffospyris didiceros* (Ehrenberg): Goll, 1969, p. 332, pl. 60, figs. 5–7, 9, text–fig. 2.

*Giraffospyris didiceros* (Ehrenberg) group: Riedel and Sanfilippo, 1970, pl. 5, figs. 3–5.

*Dendropyris didiceros* (Ehrenberg) group: Petrushevskaya and Kozlova, 1972, p. 532, pl. 40, fig. 12.
<figure><img src="image_105.png" /></figure>

**Liriospyris clathrata** (Ehrenberg, 1854) **group**

We include in this group cephalospyridids with a large variation in terms of test thickness, size of pores, size and number of feet and spines.

<figure><img src="image_106.png" /></figure>

**"Petalospyris anthocyrtoides"** Bütschli, 1882 **group**

Two-segmented nassellarians with a weak collar stricture line between the cephalis and thorax. Cephalis appears to be poreless. One row of pores is developed under the lumbar stricture from which extend several spiny feet or spines.
Synonymy list

*Liriospyris clathrata* (Ehrenberg) group

*Dictyospyris clathrus* Ehrenberg, 1854, pl. 36, fig. 25B

*Dictyospyris clathrata* Ehrenberg, 1874, p. 224; 1876, p. 68, pl. 19, fig. 7; Bütschli, pl. 32, figs. 10a, b.

*Liriospyris clathrata* (Ehrenberg): Goll, 1968, p. 175, figs. 12, 13, 16, 17; Ling, 1975, p. 726, pl. 7, figs. 6–9.

**Remarks:** There is large morphological variation in this group which leads us to determine this as a group of species.

*"Petalospyris anthocyrtoides"* (Bütschli, 1882) group

*Petalospyris anthocyrtoides* Bütschli 1882, p.532, 538, pl.32, figs.19a-c.

**Remarks:** There are some morphological variations within this group which leads us to determine this as a group of species.
<figure><img src="image_107.png" /></figure>

## Unknown cephalospyridid group

A rather sphaerical morphotype ca. 100 µm in diameter, with a thick walled test and rough test surface. Pores are variable in size, but mostly large. The cephalic lobes are bulbous and not always identical to each another. Feet or teeth-like structures taper distally.

<figure><img src="image_108.png" /></figure>

## Cephalospyridid group A

Cephalospyridids with or without horn and variable number of feet. Most cephalospyriids included in this class are unknown or rare species, but some of them may be identified as *Dorcadospyris ombros* Nigrini et al., 2006 and *Triceraspyris articulata* (Ehrenberg, 1874).
<figure><img src="image_109.png" /></figure>

### Cephalospyridid group B

We placed here all thick-walled *Desmospyris* species with a partly developed thorax. The encountered specimens vary in shape. Some of them may be identified as *Desmospyris obtusus* (Bütschli, 1882) group. Many specimens display cephalic lobes of irregular shape; some specimens bear constricted small feet or teeth-like structures and others have tiny apical horns. The pore size and arrangement is often irregular. We also included here some forms belonging to the genus *Petalospyris*, alike *Petalospyris confluens* Ehrenberg, 1874.

<figure><img src="image_110.png" /></figure>

### Cephalospyridid group C

Cephalospyridids with smooth test, bearing a short conical horn and 2 – 3 straight feet placed with a ca. 45° angle with respect to the base of the cephalis.

<figure><img src="image_111.png" /></figure>

### Cephalospyridid group D

Included species in this class are *Petalospyris argiscus* Ehrenberg, 1874 and *Thamnospyris fragoides* (Sanfilippo and Riedel, 1973).
Family **Stephaniidae** Haeckel, 1882

Genus *Zygocircus* Bütschli, 1882

<figure><img src="image_112.png" /></figure>

**Zygocircus** spp.

We placed here all *Zygocircus* species found in our material, including some that resemble
to *Z. butschlii*, but they are smaller and lack a small corner ring.
<figure><img src="image_113.png" /></figure>

## Smooth surfaced Acanthodesmioidea

Bilobate forms with a D-shaped sagittal ring, with or without feet and some of which may display a tiny horn. Some have a rough test surface, while others are rather smooth. Encountered specimens in our material belong to either Acanthodesmiidae or Cephalospyridae..

<figure><img src="image_114.png" /></figure>

## Spinose Acanthodesmioidea

Bilobate forms that display well developed spines, horns and feet, some of which may be identified at the species level (i.e. *Ceratospyris fibula* Ehrenberg, 1874).

<figure><img src="image_115.png" /></figure>

## Juvenil or broken Acanthodesmioidea

Juvenil or broken forms displaying a sagittal D-ring.
Superfamily **THEOPILIOIDEA** Haeckel, 1882

Family **Theopilioidea** Haeckel, 1882

Genus *Anthocyrtis* Ehrenberg, 1846

<figure><img src="image_116.png" /></figure>

***Anthocyrtis mespilus*** (Ehrenberg, 1847) ***group***

Two-segmented nassellarians with a weak collar stricture line between the cephalis and thorax. Cephalis seems to be mostly poreless. Several spiny feet or spines replacing the abdomen with maximum one pore row under the lumbar stricture.

Superfamily STICHOPIROIDEA Haeckel, 1882

Family **Stichopiliidae** Haeckel, 1882

Genus *Lophoconus* Haeckel, 1887

<figure><img src="image_117.png" /></figure>

*Lophoconus antilope* (Ehrenberg, 1874)

*Not trained by the CNN*
Synonymy list

***Anthocyrtis mespilus*** Ehrenberg group

*Anthocyrtis mespilus* Ehrenberg, 1847, p. 55, fig. 9; 1854, pl. 36, fig. 13; 1876, p. 66, pl. 6, figs. 4, 5; Ogane et al., 2009, pl. 50, figs. 2a, 2b, pl. 80, figs. 5a-5e, pl. 81, figs. 1a-2d; Kamikuri, 2015, pl. 10, figs. 4a-6.

*Anthocyrtis furcata* Ehrenberg, 1874, p. 216; 1876, p. 64, pl. 6, fig. 2; Funakawa et al., 2006, p. 38, pl. P13, figs. 5a, 5b; Ogane et al., 2009, pl. 80, figs. 4a-4f.

**Remarks:** There are a lot of morphological variabilities within this group which leads us to determine this as a group of species.

***Lophoconus antilope*** (Ehrenberg, 1874)

*Lophoconus antilope* (Ehrenberg, 1874), p. 308.
Superfamily **PLAGIACANTHOIDEA** Hertwig, 1879

Family **Ceratocyrtidae** Petrushevskaya, 1981

Genus *Ceratocyrtis* Bütschli, 1882

<figure><img src="image_118.png" /></figure>

**Ceratocyrtis** spp.

We placed here various *Ceratocyrtis* species, Including *Ceratocyrtis ampliata* (Ehrenberg, 1874).
<figure><img src="image_119.png" /></figure>

*Lithomelissa ehrenbergi* Buetschli, 1882 **group**

We placed here all two-segmented lophophaeniids with two to three long bladed lateral wings extending from the thorax. A bladed horn is present in most specimens. Pores are small, numerous and arranged irregularly.

<figure><img src="image_120.png" /></figure>

*Lithomelissa macroptera* Ehrenberg, 1874
Synonymy list

*Lithomelissa ehrenbergi Bütschli, 1882 group*

*Lithomelissa ehrenbergi* Bütschli, 1882, p. 517, pl. 33, fig. 21a, 21b.

**Remarks:** There are a lot of morphological variabilities within this group which leads us to determine this as a group of species.

*Lithomelissa macroptera Ehrenberg, 1874*

*Lithomelissa macroptera* Ehrenberg, 1874, p. 241; 1876, p. 78, pl. 3, figs. 8-10; Ogane et al., 2009, pl. 4, figs. 3a-3c, 4a-4d, 7a-7d, pl. 19, figs. 6a-6d; Kamikuri, 2015, pl. 11, figs. 16, 17.
Family **Lophophaenidae** Haeckel, 1882

Genus *Lophophaena* Ehrenberg, 1847

<figure><img src="image_121.png" /></figure>

**Lophophaena capito** (Ehrenberg, 1874) **group**

Lophophaeniids with a distinctly inflated large cephalis, which displays a constricted bottleneck at its base. A small conical apical horn is often present.

*Not trained in the CNN*

<figure><img src="image_122.png" /></figure>

**Lophophaena radians** Ehrenberg, 1874 **group**

We placed here all loophophaenids with several apical horns or spines that are sometimes distally and medially attached like a trabecule. There are mainly three different morphotypes present here, all of them considered as part of the species variability, including typical *L. radians* with large pores and a rather distinct collar stricture. There are morphotypes that have less distinct collar strictures and pores of equal size that are smaller and more sparsely distributed.
Synonymy list

*Lophophaena capito* Ehrenberg group

*Lophophaena capito* Ehrenberg, 1874, p. 242; 1876, p. 78, pl. 8, fig. 6; Ogane et al., 2009, pl. 19, figs. 8a-8c, pl. 34, figs. 3a-3c, pl. 79, figs. 2a-2c.

*Lophophaena ? capito* Ehrenberg group: Petrushevskaya and Kozlova, 1972, p. 535, pl. 33, figs. 20-23.

*Lophophaena capito* Ehrenberg group: Funakawa et al., 2006, p. 20, Pl. P3, figs. 3-4.

*Lophophaena radians* Ehrenberg group

*Lophophaena radians* Ehrenberg, 1874, p. 243; 1876, p. 78, pl. 8, figs. 7-9; Ogane et al., 2009, pl. 3, figs. 3a-3e, 5a-5d, pl. 79, figs. 4a-4c; Funakawa et al., 2006, p. 20, pl. P3, figs. 5a-6b; Kamikuri, 2015, pl. 13, figs. 30a, 30b; Meunier and Danelian, 2022, p. 21, pl. 3, fig. 1.
<figure><img src="image_123.png" /><figcaption>*Lophophaena spp.*
Two segmented lophophaenids bearing a short horn.</figcaption></figure>
Family **Phaenocalpididae** Haeckel, 1887

Genus *Rhabdolithis* Ehrenberg, 1847

<figure><img src="image_124.png" /><figcaption>*Rhabdolithis pipa* Ehrenberg, 1854</figcaption></figure>

50 µm
Synonymy list

*Rhabdolithis pipa* Ehrenberg

*Rhabdolithis pipa* Ehrenberg, 1854, pl. 36, fig. 59; 1876, p. 159, pl. 1, fig. 27; Sanfilippo and Riedel, 1973, p. 529, pl. 18, figs. 12–16, pl. 33, figs. 9, 10.
<figure><img src="image_125.png" /></figure>

### Plagiacanthoidea group A

Unknown plagiacanthoidea.

<figure><img src="image_126.png" /></figure>

### Plagiacanthoidea group B

Two-segmented forms with two to three long feet and a strong bladed horn..

<figure><img src="image_127.png" /></figure>

### Plagiacanthoidea group C

Two-segmented forms that are often without any spines and only exceptionally display several small feet.
Superfamily PYLOBOTRYDOIDEA Haeckel, 1882

Family Pylobotrydidae Haeckel, 1882

<figure><img src="image_128.png" /></figure>

Pylobotrydid group

Two-segmented nassellarians with a cephalis that is subdivided into ante-, eu-, and postcephalic lobes.
Superfamily **CYCLADOPHOROIDEA** Suzuki, 2019

Family **Cycladophoridae** Suzuki, 2019

Genus *Cycladophora* Ehrenberg, 1846

<figure><img src="image_129.png" /><figcaption>**Cycladophora spatiosa** (Ehrenberg, 1874) **group**</figcaption></figure>

Three segmented cycladophorids that are overall conical in shape and display a globose cephalis, a campanulate thorax and a truncated conical abdomen that is wide open distally.
Pores are rather large and well-organised in rows.
Synonymy list

*Cycladophora spatiosa* Ehrenberg group

*Cycladophora spatiosa* Ehrenberg, 1847, p. 48; 1874, p. 222; 1876, p. 68, pl. 18, figs. 5, 6; Haeckel, 1887, p. 1379; Ogane et al., 2009, pl. 9, figs. 6a, 6b, pl. 87, figs. 4a, 4b; Kamikuri, 2015, pl. 10, figs. 7a, 7b.

*Cycladophora spatiosa* (Ehrenberg) group: Funakawa et al., 2006, p. 38, pl. P13, figs. 7a–8b.

*Anthocyrtella spatiosa* (Ehrenberg): Petrushevskaya and Kozlova, 1972, p. 541, pl. 33, figs. 1–3.
<figure><img src="image_130.png" /></figure>

*Dictyophimus craticula* Ehrenberg, 1874

*Not trained by the CNN*

Family Lithochytrididae Ehrenberg, 1846

Genus *Lithochytris* Ehrenberg, 1846

<figure><img src="image_131.png" /></figure>

*Lithochytris vespertilio* Ehrenberg, 1874

50 µm

Genus *Sethochytris* Haeckel, 1882

<figure><img src="image_132.png" /></figure>

*Sethochytris triconiscus* Haeckel, 1887

<figure><img src="image_133.png" /></figure>

50 µm
Synonymy list

*Dictyophimus craticula* Ehrenberg

*Dictyophimus craticula* Ehrenberg, 1874, p. 223; 1876, p. 68, pl. 5, figs. 4, 5; Sanfilippo and Riedel, 1973, p. 529, pl. 19, fig. 1 (*partim*); Ogane et al., 2009, pl. 21, fig. 5, pl. 36, figs. 1a-1f, pl. 37, figs. 2a-4; Meunier and Danelian, 2022, p. 19, pl. 3, fig. 6.

*Lithochytris vespertilio* Ehrenberg

*Lithochytris vespertilio* Ehrenberg, 1874, p. 239; 1876, p. 76, pl. 4, fig. 10; Riedel and Sanfilippo, 1970, p. 518, pl. 9, figs. 8, 9; Ogane et al., 2009, pl. 45, figs. 1a-3e; Meunier and Danelian, 2022, p. 20, pl. 3, fig. 4.

*Sethochytris triconiscus* Haeckel

*Sethochytris triconiscus* Haeckel, 1887, p. 1239, pl. 57, fig. 13; Riedel and Sanfilippo, 1970, p. 528, pl. 9, figs. 6; Meunier and Danelian, 2022, p. 22, pl. 1, fig. 15.
<figure><img src="image_134.png" /><figcaption>*Lychnocanoma bajunensis* Renz, 1984</figcaption></figure>

<figure><img src="image_135.png" /><figcaption>*Lychnocanium babylonis* (Clark and Campbell, 1942) group</figcaption></figure>

We placed here all lithochytridiids with a conical horn that covers most of or the entire cephalis and bear three smooth non-bladed feet.

<figure><img src="image_136.png" /><figcaption>*Lychnocanium carinatum*</figcaption></figure>

**Lithochytridid group**

We included in this class all two-segmented lithochytridids with a horn, other than *Lychnocanissa bajunensis*, *Lychnocanium babylonis*, or *Lithochytris* spp. bearing porous feet. An example of an identified species is *Lychnocanium carinatum* Ehrenberg, 1874.
Synonymy list

*Lychnocanoma bajunensis* Renz

*Lychnocanoma bajunensis* Renz 1984, p. 459, pl. 1, figs. 4–6.

*Lychnocanium babylonis* (Clark and Campbell) group

? *Lychnocanium tribulus* Ehrenberg, 1874, p. 245; 1876, p. 80, pl. 7, fig. 1.

*Dictyophimus (Dictyophimium) babylonis* Clark and Campbell, 1942, p. 67, pl. 9, figs. 32, 36.

*Sethochytris babylonis* (Clark and Campbell) group: Riedel and Sanfilippo, 1970, p. 528, pl. 9, figs. 1–3.
Superfamily **PTEROCORYTHOIDEA** Haeckel, 1882

Family **Lophocyrtiidae** Sanfilippo and Caulet in De Wever et al., 2001

Genus *Aphetocyrtis* Sanfilippo and Caulet, 1998

<figure><img src="image_137.png" /><figcaption>*Aphetocyrtis zamenhofi* Meunier and Danelian, 2023</figcaption></figure>

Genus *Apoplanius* Sanfilippo & Caulet 1998

<figure><img src="image_138.png" /></figure>

**Apoplanius** spp.

Included forms are *Apoplanius keraspera* (Sanfilippo and Caulet, 1998) and *Apoplanius aspera* (Ehrenberg, 1874).
Synonymy list

*Aphetocyrtis zamenhofi* Meunier and Danelian

*Theocorys* sp. Riedel and Sanfilippo, 1977, pl. 7, fig. 9.

*Aphetocyrtis zamenhofi* Meunier and Danelian, 2023, p. 17, figs. 6.1–6.4
<figure><img src="image_139.png" /><figcaption>*Lophocyrtis alauda* (Ehrenberg, 1874)</figcaption></figure>

<figure><img src="image_140.png" /><figcaption>*Lophocyrtis barbadense* (Ehrenberg, 1874)</figcaption></figure>

*Not trained by the CNN*
Synonymy list

*Lophocyrtis alauda* (Ehrenberg)

*Eucyrtidium alauda* Ehrenberg, 1874, p. 225; 1876, p. 70, pl. 9, fig. 4; Ogane et al., 2009, pl. 49, figs. 1a-1e.

*Lophocyrtis* (*Lophocyrtis* ?) cf.. *semipolita*: Sanfilippo and Caulet, 1998, p. 10, pl. 4, fig. 7.

*Lophocyrtis* (*Lophocyrtis* ?) *alauda* (Ehrenberg): Meunier and Danelian, 2022, p. 20, pl. 1, fig. 16.

*Lophocyrtis barbadense* (Ehrenberg)

*Pterocanium barbadense* Ehrenberg, 1874, p. 254; 1876, pl. 17, fig. 6.

*Lophocyrtis* (*Lophocyrtis* ?) *barbadense* (Ehrenberg): Sanfilippo and Caulet, 1998, p. 8, pl. 4, figs. 9, 10a, 10b; Funakawa et al., 2006, p. 26, pl. P8, figs. 4a-5b.
<figure><img src="image_141.png" /></figure>

### Lophocyrtiid group A

Rare lophocyrtids. At least one in this group is *Apoplanius klydus* (Sanfilippo and Cauet, 1998)

*Not trained by the CNN*

<figure><img src="image_142.png" /></figure>

### Lophocyrtiid group B

The lophocyrtids in this group have fewer and larger pores, some forms have a very long and curvy abdomen. Included forms are *Aphetocyrtis? columboi* Meunier and Danelian, 2023 and *Lophocyrtis attenuata* (Ehrenberg, 1874). It is possible that there are other species included in this group as well.
<figure><img src="image_143.png" /></figure>

**Albatrossidium** spp.

Pterocorythids with a relatively large cephalis. Pores of the thorax similar to those of the abdomen. Abdomen in this class is often short or absent. A well-defined collar constriction exists between thorax and cephalis. Presence of a broad-based apical horn.

<figure><img src="image_144.png" /><figcaption>*Calocyclas hispida* (Ehrenberg, 1874)</figcaption></figure>

50 µm

<figure><img src="image_145.png" /><figcaption>*Calocycloma ampulla* (Ehrenberg, 1854)</figcaption></figure>

50 µm
Synonymy list

*Calocyclas hispida* (Ehrenberg)

*Anthocyrtis hispida* Ehrenberg, 1874, p. 216; 1876, p. 64, pl. 8, fig. 2; Ogane et al., 2009, pl. 2, figs. 7a-9c, pl. 50, figs. 4a, 4b.

*Cycladophora hispida* (Ehrenberg): Riedel and Sanfilippo, 1970, p. 529, pl. 10, fig. 9; Moore, 1971, p. 741, pl. 4, figs. 6, 7.

*Calocyclas hispida* (Ehrenberg): Foreman, 1973, p. 434, pl. 1, figs. 12-15, pl. 9, fig. 18; Sanfilippo and Blome, 2001, p. 210, fig. 6g.

*Calocycloma ampulla* (Ehrenberg)

*Eucyrtidium ampulla* Ehrenberg, 1854, p. 21, pl. 36, figs. 15a-15c; 1874, p. 225; 1876, p. 70, pl. 10, figs. 11, 12; Ogane et al., 2009, pl. 60, figs. 1a-3d.

*Calocycloma ? ampulla* (Ehrenberg): Riedel and Sanfilippo, 1970, p. 524, pl. 6, fig. 1.

*Calocycloma ampulla* (Ehrenberg): Foreman, 1973, p. 434, pl. 1, figs. 1-5, pl. 9, fig. 20.
<figure><img src="image_146.png" /><figcaption>*Phormocyrtis embolum* Ehrenberg, 1874</figcaption></figure>

<figure><img src="image_147.png" /><figcaption>*Phormocyrtis lazari* Meunier and Danelian, 2023</figcaption></figure>
Synonymy list

*Phormocyrtis embolum* (Ehrenberg)

*Eucyrtidium embolum* Ehrenberg, 1874, p. 228; 1876, p. 70, pl. 10, fig. 5; Johnson, 1974, p. 548, pl. 4, fig. 5 (*partim*.); Nigrini, 1974, p. 1068, pl. 1H, figs. 4, 5; Ogane et al., 2009, pl. 22, figs. 6a–6c.

*Phormocyrtis embolum* (Ehrenberg): Haeckel, 1887, p. 1369; Riedel, 1957, p. 88, pl. 3, fig. 6 (*partim*); Meunier and Danelian, 2022, p. 21, pl. 2, fig. 12.

*Phormocyrtis lazari* Meunier and Danelian

*Phormocyrtis lazari* Meunier and Danelian, 2023.
<figure><img src="image_148.png" /><figcaption>*Podocyrtis (Lampterium) chalara* Riedel and Sanfilippo, 1970</figcaption></figure>

<figure><img src="image_149.png" /><figcaption>*Podocyrtis (Lampterium) goetheana* (Haeckel, 1887)</figcaption></figure>
Synonymy list

*Podocyrtis (Lampterium) chalara* Riedel and Sanfilippo

*Podocyrtis (Lampterium) chalara* Riedel and Sanfilippo, 1970, p. 535, pl. 12, figs. 2, 3; Riedel and Sanfilippo, 1978, p. 71, pl. 8, fig. 3; Sanfilippo et al., 1985, p. 697, fig. 30.11

*Podocyrtis (Lampterium) goetheana* (Haeckel)

*Cycladophora goetheana* Haeckel, 1887, p. 1376, pl. 65, fig. 5.

*Podocyrtis (Lampterium) goetheana* (Haeckel): Riedel and Sanfilippo, 1970, p. 535; Sanfilippo et al., 1985 p. 697, fig. 30.12; Nigrini et al., 2005, p. 45, pl. P5, figs. 11, 12.
<figure><img src="image_150.png" /></figure>

**Podocyrtis (Lampterium) mitra** Ehrenberg, 1854 **group**

Included here are also specimens belonging to *Podocyrtis (Lampterium) trachodes* Riedel and Sanfilippo, 1970.

<figure><img src="image_151.png" /></figure>

**"Podocyrtis (Lampterium) puellasinensis"** Ehrenberg, 1874

Two segmented pterocorytoiids (cephalis and thorax with a missing abdomen) that are conical in shape. It's possible that some of these are or parts of *Podocyrtis Lampterium* spp.

<figure><img src="image_152.png" /></figure>

**Podocyrtis (Lampterium) sinuosa** Ehrenberg, 1874
Synonymy list

*Podocyrtis (Lampterium) mitra* Ehrenberg group

*Podocyrtis mitra* Ehrenberg, 1854, p. 21, pl. 36, fig. 20.

*Podocyrtis (Lampterium) mitra* Ehrenberg: Riedel and Sanfilippo, 1970, p. 534, pl. 11, figs. 5, 6; Riedel and Sanfilippo, 1978, pl. 8, fig. 7; Sanfilippo et al., 1985, p. 698, fig. 30.10; Sanfilippo and Blome, 2001, p. 215, figs. 10a, 10b.

*Podocyrtis (Lampterium) trachodes* Riedel and Sanfilippo, 1970, p. 535, pl. 11, fig. 7; pl. 12, fig. 1; Sanfilippo et al., 1985, p. 699, fig. 30.14; Sanfilippo and Blome; 2001, p. 215, fig. 10c.

*"Podocyrtis (Lampterium) puellasinensis"* Ehrenberg

*Podocyrtis puella sinensis* Ehrenberg, 1874, p. 252; 1876, p. 82, pl. 14, fig. 3.

*Podocyrtis puella-sinensis* Ehrenberg [sic.]: Ogane et al., 2009, pl. 48, figs. 9a-9f.

*Podocyrtis (Lampterium) puellasinensis* Ehrenberg: Meunier and Danelian, 2023, p. 21, figs. 7.9-7.11.

*Podocyrtis (Lampterium) sinuosa* Ehrenberg

*Podocyrtis sinuosa* Ehrenberg, 1874, p. 253; 1876, p. 82, pl. 15, fig. 5.

*Podocyrtis (Lampterium) sinuosa* Ehrenberg: Riedel and Sanfilippo, 1970, p. 534, pl. 11, figs. 3, 4; Sanfilippo et al., 1985, p. 698, fig. 30.9.
<figure><img src="image_153.png" /><figcaption>*Podocyrtis (Podocyrtis) papalis* Ehrenberg, 1847</figcaption></figure>

<figure><img src="image_154.png" /><figcaption>*Podocyrtis (Podocyrtoges) ampla* Ehrenberg, 1874</figcaption></figure>
Synonymy list

*Podocyrtis (Podocyrtis) papalis* Ehrenberg

*Podocyrtis papalis* Ehrenberg, 1847, p. 55, fig. 2; 1854, p. 21, pl. 36, fig. 23; 1874; p. 251; 1876, p. 82, pl. 25, fig. 6.

*Podocyrtis (Podocyrtis) papalis* Ehrenberg: Riedel and Sanfilippo, 1970, p. 533, pl. 11, fig. 1; Sanfilippo and Riedel, 1973, p. 531, pl. 20, figs. 11–14; pl. 36, figs. 2, 3.

*Podocyrtis (Podocyrtoges) ampla* Ehrenberg

*Podocyrtis ? ampla* Ehrenberg, 1874, p. 248; 1876, p. 80, pl. 16, fig. 7.

*Podocyrtis (Podocyrtis) ampla* Ehrenberg: Riedel and Sanfilippo, 1970, p. 533, pl. 12, figs. 7, 8.

*Podocyrtis (Podocyrtoges) ampla* Ehrenberg: Sanfilippo and Riedel, 1992, p. 14, pl. 5, fig. 4.
<figure><img src="image_155.png" /></figure>

*Podocyrtis (Podocyrtoges) diamesa* Riedel and Sanfilippo, 1970

<figure><img src="image_156.png" /></figure>

*Podocyrtis (Podocyrtoges) phyxis* Sanfilippo and Riedel, 1973

Subgenus *Podocyrtopsis* Sanfilippo and Riedel, 1992

<figure><img src="image_157.png" /></figure>

*Podocyrtis (Podocyrtopsis) apeza* Riedel and Sanfilippo, 1970

*Not trained by the CNN*
Synonymy list

*Podocyrtis (Podocyrtoges) diamesa* Riedel and Sanfilippo

*Podocyrtis (Podocyrtis) diamesa* Riedel and Sanfilippo, 1970, p. 533 pl. 12, fig. 4 (*partim.*);
Sanfilippo and Riedel, 1973, p. 531, pl. 20, figs. 9, 10, pl. 35, figs. 10, 11.

*Podocyrtis (Podocyrtoges) diamesa* Sanfilippo and Riedel, 1992, p. 14; Nigrini et al., 2005, p. 46, pl. P5, fig. 10.

*Podocyrtis (Podocyrtoges) phyxis* Sanfilippo and Riedel

*Podocyrtis (Podocyrtis) diamesa* Riedel and Sanfilippo, 1970, p. 533, pl. 12, fig. 6 (*partim*). *Podocyrtis (Podocyrtis) phyxis* Sanfilippo and Riedel, 1973, p. 531.

*Podocyrtis (Podocyrtoges) phyxis* Sanfilippo and Riedel: Sanfilippo and Riedel, 1992, p. 14.

*Podocyrtis (Podocyrtopsis) apeza* Sanfilippo and Riedel

*Podocyrtis (Podocyrtopsis) apeza* Sanfilippo and Riedel, 1992, p. 14, pl. 3, figs. 13–15;
Moore and Kamikuri, 2012, p. 10, pl. P7, fig. 7; Meunier and Danelian, 2022, p. 22, pl. 2, fig.
10.
<figure><img src="image_158.png" /><figcaption>*Theocyrtis scolopax* (Ehrenberg, 1874)</figcaption></figure>

<figure><img src="image_159.png" /><figcaption>*Theocyrtis* spp.</figcaption></figure>

Pterocorythiids with several tightly packed pores. Wide conical-tubular cephalis, sometimes bearing a short conical horn. Conical to campanulate thorax. Abdomen more or less complete.
Synonymy list

*Theocyrtis scolopax* (Ehrenberg)

*Eucyrtidium scolopax* Ehrenberg, 1874, p. 232; 1876, p. 72, pl. 9, fig. 5; Ogane et al., pl. 58, figs. 3a–3f.

*Theocyrtis scolopax* (Ehrenberg): Popova et al., 2002, p. 50, fig. 14G.
<figure><img src="image_160.png" /></figure>

*Pterocodon apis* Ehrenberg, 1874

*Not trained by the CNN*

Genus *Theocorys* Haeckel, 1882

<figure><img src="image_161.png" /></figure>

*Theocorys spp.*

Included in this class are *Theocorys anaclasta* Riedel and Sanfilippo, 1970 and *Theocorys anaphographa* Riedel and Sanfilippo, 1970

Genus *Theocotyle* Riedel and Sanfilippo, 1970

<figure><img src="image_162.png" /></figure>

*Theocotyle* spp.

Included in this class are *Theocotyle venezuelensis* Riedel and Sanfilippo, 1970 and *Theocotyle cryptocephala* (Ehrenberg).
Synonymy list

*Pterocodon apis* Ehrenberg, 1874

*Pterocodon apis* Ehrenberg, 1874, p. 255.
Family **Theocotylidae** Petrushevskaya, 1981

Genus *Thyrsocyrtis* Ehrenberg, 1847

Subgenus *Pentalocorys* Haeckel, 1882

<figure><img src="image_163.png" /><figcaption>*Thyrsocyrtis (Pentalocorys) krooni* (Ehrenberg, 1874)</figcaption></figure>

*Not trained by the CNN*

<figure><img src="image_164.png" /></figure>

<figure><img src="image_165.png" /></figure>

<figure><img src="image_166.png" /></figure>

<figure><img src="image_167.png" /><figcaption>*Thyrsocyrtis (Pentalocorys) triacantha* (Ehrenberg, 1874)</figcaption></figure>

Subgenus *Thyrsocyrtis* Ehrenberg, 1847

<figure><img src="image_168.png" /></figure>

<figure><img src="image_169.png" /></figure>

<figure><img src="image_170.png" /></figure>

<figure><img src="image_171.png" /><figcaption>*Thyrsocyrtis (Thyrsocyrtis) rhizodon* Ehrenberg, 1874</figcaption></figure>
Synonymy list

*Thyrsocyrtis (Pentalacorys) krooni Sanfilippo and Blome*

*Thyrsocyrtis tetracantha* (Ehrenberg): Riedel and Sanfilippo, 1978, p. 81, pl. 10, fig. 9 (partim.).

*Thyrsocyrtis (Pentalacorys) tetracantha* (Ehrenberg): Sanfilippo and Riedel, 1982, p. 176 (partim.), pl. 1, fig. 11.

*Thyrsocyrtis (Pentalacorys) krooni* Sanfilippo and Blome, 2001, p. 207, figs. 7a–7e; Moore and Kamikuri, 2012, p. 11, pl. P8, figs. 7, 8; Meunier and Danelian, 2022, p. 23, pl. 2, fig. 14.

*Thyrsocyrtis (Pentalacorys) triacantha* (Ehrenberg)

*Podocyrtis triacantha* Ehrenberg, 1874, p. 254; 1876, p. 82, pl. 13, fig. 4

*Thyrsocyrtis (Pentalacorys) triacantha* (Ehrenberg): Sanfilippo and Riedel, 1982, p. 176, pl. 1, figs. 8–10, pl. 3, figs. 3, 4.

*Thyrsocyrtis (Thyrsocyrtis) rhizodon* Ehrenberg

*Thyrsocyrtis rhizodon* Ehrenberg, 1874, p. 262; 1876, p. 84, pl. 12, fig. 1; Sanfilippo and Riedel, 1982, p. 173, pl. 1, figs. 14–16; pl. 3, figs. 12–17.

*Thyrsocyrtis (Thyrsocyrtis) rhizodon* Ehrenberg: Kamikuri, 2015, pl. 19, figs. 6a, 6b.
<figure><img src="image_172.png" /></figure>

**Eusyringium fistuligerum** (Ehrenberg, 1874) **group**

All *Eusyringium* species with a distal tube is placed here no matter if there is or not a stricture present between the thorax and the tube. Our specimens don't have any strictures but since there are a lot of different *Eusyringium* species and variations described and published by different authors we decided to refer all or *Eusyringium* with distal tubes as *E. fistuligerum* group.

<figure><img src="image_173.png" /></figure>

*Eusyringium lagena* (Ehrenberg, 1874)
Synonymy list

*Eusyringium fistuligerum* (Ehrenberg) group

*Eucyrtidium tubulus* Ehrenberg, 1854, p. 21, pl. 36, fig. 19; 1874, p. 233; 1876, p. 72, pl. 9, fig. 6.

*Eusyringium tubulus* (Ehrenberg): Ling, 1975, p. 729, pl. 9, fig. 22.

*Eucyrtidium fistuligerum* Ehrenberg, 1874, p. 229; 1876, p. 70, pl. 9, fig. 3.

*Eusyringium fistuligerum* (Ehrenberg): Riedel and Sanfilippo, 1970, p. 527, pl. 8, figs. 8?, 9; Moore, 1971, p. 741, pl. 4, fig. 10–11; Petrushevskaya and Kozlova, 1972, p. 549, pl. 32, fig. 3–5; Ling, 1975, p. 728, pl. 9, figs. 19, 20; Sanfilippo and Blome, 2001, p. 212, fig. 9a–d.

*Eusyringium lagena* (Ehrenberg)

*Lithopera lagena* Ehrenberg, 1874, p. 241; 1876, p. 78, pl. 3, fig. 4.

*Eusyringium lagena* (Ehrenberg): Riedel and Sanfilippo, 1970, p. 527, pl. 8, figs. 5–7; Foreman, 1973, p. 435, pl. 11, figs. 4, 5.
<figure><img src="image_174.png" /></figure>

**Rhopalocanium ornatum** (Ehrenberg, 1874) **group**

Three-segmented pterocorythoids with two to three lateral wings. On some specimens the width of the abdomen narrows down distally to form a tube.

<figure><img src="image_175.png" /></figure>

**Rhopalocanium sphinx** (Ehrenberg, 1874)

*Not trained by the CNN*

<figure><img src="image_176.png" /></figure>

**Pterocorythoid group**

Three-segmented Pterocorythoids, displaying an elongated campanulate cephalis and a campanulate to inflated spherical thorax. The abdomen is sometimes not fully developed, in some other specimens it is more cylindrical and tapering distally, on others it is slightly inflated. The length of the abdomen is shorter or equal to the length of the thorax.
Synonymy list

*Rhopalocanium ornatum* Ehrenberg group

*Rhopalocanium ornatum* Ehrenberg, 1847, fig. 3; 1854, pl. 36, fig. 9; 1874, p. 256; 1876, p. 82, pl. 17, fig. 8; Foreman, 1973, p. 439, pl. 2, figs. 8–10, pl. 12, fig. 3; Riedel and Sanfilippo, 1978, p. 72, pl. 9, fig. 5; Sanfilippo and Blome, 2001, p. 217, figs. 10o, 10p.

**Remarks:** There are a lot of morphological variabilities within this group which leads us to determine this as a group of species.

*Rhopalocanium sphinx* (Ehrenberg, 1874)

*Rhopalocanium sphinx* (Ehrenberg, 1874); p. 255.
<figure><img src="image_177.png" /></figure>

Nassellaria group A

A trash group of nassellarians.

50 µm
<figure><img src="image_178.png" /></figure>

### Nassellaria group B

Pterocorythids with a large cephalis. Pores of the thorax are similar to those of the abdomen. Many specimens have a long abdomen, no horn and a discrete outline (weak collar stricture) between the thorax and cephalis.

<figure><img src="image_179.png" /></figure>

### Nassellaria group C

Small nassellarians with two to three segments that bear two lateral wings expanding from the thorax. The abdomen of some specimens is very long and cylindrical.

Not trained by the CNN.
Spumellarians
Superfamily **LITHOCYCLIOIDEA** Ehrenberg, 1846

Family **Lithocycliidae** Ehrenberg, 1846

Genus *Lithocyclia* Ehrenberg, 1847

<figure><img src="image_180.png" /><figcaption>*Lithocyclia ocellus* Ehrenberg, 1854 *group*</figcaption></figure>

Family **Phacodiscidae** Haeckel, 1882

Genus *Periphaena* Ehrenberg, 1874

<figure><img src="image_181.png" /><figcaption>*Periphaena decora* Ehrenberg, 1874</figcaption></figure>

Some of the encountered forms display small equatorial spines..
Synonymy list

*Lithocyclia ocellus group*

*Lithocyclia ocellus* Ehrenberg group

*Lithocyclia ocellus* Ehrenberg, 1854b, pl. 36, fig. 30; Petrushevskaya and Kozlova, 1972, p. 523, pl. 15, figs. 1-2.

*Lithocyclia ocellus* Ehrenberg group: Riedel and Sanfilippo, 1970, p. 522, pl. 5, figs. 1-2; Riedel and Sanfilippo, 1971, p. 1588, pl. 3A, fig. 6; Kamikuri, 2015, pl. 19, fig. 2.

*Periphaena decora* Ehrenberg

*Periphaena decora* Ehrenberg, 1874, p. 246; 1876, p. 80, pl. 28, fig. 6; Petrushevskaya and Kozlova, 1972, p. 523, pl. 14, figs. 1, 2; Sanfilippo and Riedel, 1973, p. 523, pl. 8, figs. 8-10, pl. 27, figs. 2-4 (partim.); Nigrini, 1974, p. 1065, pl. 1C, figs. 1, 2, 4, 6 (partim.); Funakawa et al., 2006, p. 42, pl. P15, figs. 5a-6b; Ogane et al., 2009, pl. 14, figs. 6a-6c, pl. 30, fig. 4, pl. 67, figs. 3a, 3b.
Superfamily **LITHELIOIDEA** Haeckel, 1862

Family **Litheliidae** Haeckel, 1862

Genus *Middourium* Kozlova, 1999

<figure><img src="image_182.png" /></figure>

**“Middourium” group**

We include in this class forms that are characterized by an ellipsoidal, loosely concentric shell that resembles *Middourium regulare* sensu O'Dogherty et al. (2021, p. 873). However, some of these forms may be incomplete or broken shells of *Lithelius* spp.
Superfamily STYLOSPHAEROIDEA Haeckel, 1887

Family Stylosphaeroidae Haeckel, 1887

Genus *Spongatractus* Hackel, 1887

<figure><img src="image_183.png" /><figcaption>*Spongatractus pachystylus* (Ehrenberg, 1874)</figcaption></figure>

*Not trained by the CNN*

Genus *Stylosphaera* Ehrenberg, 1846

<figure><img src="image_184.png" /><figcaption>*Stylosphaera coronata* Ehrenberg, 1874 group</figcaption></figure>

Single shelled spherical stylosphaeroids, most of which bear two polar spines of variable length and thickness. The typical *Stylosphaera coronata* have two spines, while we also encountered forms which have four spines which is why we consider this to be a possibility of being a group of species.
Synonymy list

*Stylosphaera coronata* Ehrenberg, 1874

*Stylosphaera coronata* Ehrenberg, 1874, p. 258.

**Remarks:** There are a lot of morphological variabilities within this group which leads us to determine this as a group of species.

*Spongattractus pachystylus* (Ehrenberg)

*Spongattractus pachystylus* (Ehrenberg): Sanfilippo and Riedel, 1973, p. 519, pl. 2, figs. 4-6, pl. 25, fig. 3.

*Spongosphaera pachystyla* Ehrenberg, 1874, p. 256; 1876, p. 82, pl. 26, fig. 3.
Superfamily **HELIOSATURNALOIDEA** Kozur & Mostler, 1972

Family **Axoprunidae** Dumitrica, 1985

Genus *Axoprunum* Haeckel 1887

<figure><img src="image_185.png" /><figcaption>**"Axoprunum" sp. A**</figcaption></figure>

Simple spherical spumellarians with large pores having two polar long elongated spiny
spines.

*Not trained by the CNN*
<figure><img src="image_186.png" /></figure>

## Discoidal biconvex spumellarians with equatorial spines

In this class are included forms that belong to at least two genera: *Heliodiscus* (family Heliodiscidae) and *Periphaena* (family Phocodicidae).

<figure><img src="image_187.png" /></figure>

## Spherical spumellarians with large pores

Spherical spumellarians (some of them multishelled) displaying large subcircular pores on the outer shell.

<figure><img src="image_188.png" /></figure>

## Multi-armed spongy spumellarians

In this class are included spongy spumellarians composed of a variable number of arms and a patagium developed in their central part. Spines may be present or absent. One genus included in this class is *Histiastrum* Ehrenberg 1847.
<figure><img src="image_189.png" /></figure>

<figure><img src="image_190.png" /></figure>

## Spherical spumellarians with radial spines

Spherical multishelled or spongy spumellarians with numerous (more than 8) conical rodlike spines.

*Not trained by the CNN*

<figure><img src="image_191.png" /></figure>

## Discoidal spongy spumellarians with rod shaped spines

We included in this class flat discoidal spongy shells bearing a few elongated rod-shaped spines.

<figure><img src="image_192.png" /></figure>

## Discoidal spongy spumellarians

We have included in this class discoidal forms that are composed internally of a shell that appears usually spongy or sometimes displays concentric rings.
<figure><img src="image_193.png" /></figure>

### Discoidal spumellarians with **regular** concentric rings

In this class are included forms that belong to at least two families Trematodiscidae, such as *Frustrella* and *Circodiscidae*. They either display short spines or no spines.

<figure><img src="image_194.png" /></figure>

### Discoidal spumellarians with **irregular** concentric rings

In this class are included forms that belong to at least two families Trematodiscidae displaying irregular concentric rings.

<figure><img src="image_195.png" /></figure>

### Small discoidal spumellarians with concentric rings

In this class are included forms that belong to at least the family Trematodiscidae displaying concentric rings. Some specimens can be broken central parts of larger Trematodiscids.
References

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## Abstract

Micropaleontology is not only about studying organisms that lived in the past, with implications for biostratigraphy and evolutionary changes within morphospecies. It is also about understanding Earth's past environments, paleoceanography and climate change. This field is facing numerous challenges, since the analysis of microfossils demands significant human effort and taxonomic expertise. This PhD work focuses on the application of Artificial Intelligence (AI), such as Artificial Neural Networks (ANNs), for automatic image recognition of tropical Atlantic middle Eocene radiolarians. Large datasets have been constructed, in order to train different neural networks. Our results show that neural networks can automatically classify several different radiolarian classes down to the species level; in many cases, they were able to identify closely related species and even transitional morphotypes in evolutionary lineages. It was also possible to correctly identify partly broken or blurry radiolarians. NNs were also successfully applied to automatic image recognition to be used with a biostratigraphic aim. This work includes the use of the classical neural network approaches for analysing visual context such as Convolutional Neural Networks (CNNs), but also the use of Spiking Neural Networks (SNNs), which are not as commonly used for automatic image recognition, as CNNs. SNNs resulted in nearly equal accuracy as the one obtained with CNNs, but the use of an SNN is more computational efficient and takes up less memory. There has also been a comparison between the outcome of NN training and of traditional morphometric analyses, such as Linear Discrimination Analysis (LDA), giving approximately similar results. Our research does not only simplify and speed up the analysis process, but also helps in increasing the accuracy and consistency of micropaleontological interpretations.

**Keywords:** Artificial Intelligence, Radiolaria, Neural Networks, middle Eocene, CNN, SNN, biostratigraphy

## Résumé

La micropaléontologie n'est pas seulement concernée par l'étude d'organismes qui ont vécu dans le passé, avec des implications pour la biostratigraphie et les changements évolutifs au sein des morphoespèces, mais également par la compréhension des environnements passés de la Terre, la paléoocéanographie et les changements climatiques. Ce domaine est confronté à de nombreux défis, car l'analyse des microfossiles nécessite un effort humain et une expertise taxonomique considérables. La présente thèse traite de l'application de l'intelligence artificielle (AI), tels les réseaux de neurones artificiels (RNA), pour la reconnaissance automatique d'images de radiolaires de l'Éocène moyen de l'Atlantique tropical. Des larges bases de données ont été construites afin d'entrainer différents réseaux de neurones. Nos résultats montrent que ceux-ci réussissent à classer automatiquement plusieurs classes différentes de radiolaires, jusqu'au niveau de l'espèce, ainsi que dans de nombreux cas, d'identifier des espèces étroitement apparentées et même des morphotypes transitionnels au sein des lignées évolutives. Le RNA a même pu déterminer correctement des radiolaires brisés ou des images floues. Un RNA a également pu être appliqué avec succès à la reconnaissance automatique d'images pour un travail biostratigraphique. Ce travail inclut l'utilisation des approches classiques de réseaux de neurones pour analyser le contexte visuel, tels que les réseaux neuronaux convolutifs (CNN), mais comprend également l'utilisation de réseaux de neurones à pointes (SNN), qui ne sont pas aussi couramment utilisés que les CNN pour la reconnaissance automatique d'images. Les SNN ont permis d'obtenir une précision quasi-équivalente à celle des CNN, avec la différence que leur utilisation est plus efficace en termes de calcul et a besoin de moins de mémoire. Il y a également eu comparaisons des résultats issus des RNA et d'analyses morphométrique classiques, telles que l'analyse de discrimination linéaire (LDA), avec des résultats sensiblement similaires. Notre recherche n'a pas seulement simplifié et accéléré le processus analytique, mais a également contribué à accroître la précision et la cohérence des interprétations micropaléontologiques.

**Mots clés:** Intelligence artificielle, radiolaires, réseaux de neurones, Éocène moyen, CNN, SNN, biostratigraphie