dataesr/scientific-paragraphs-categorization huggingface dataset:
Identification of species with similar environmental parameters-driven genetic connectivity To identify taxa sharing similar environmental parameters that drive their genetic connectivity, the variance explained by each factor was used with dimensional reduction through t-distributed stochastic neighbour embedding (t-SNE) using the Rtsne R package  [99]  with a perplexity of 5 and 5,000 iterations, and we extracted the taxa coordinates. Subsequently, k-means clustering was performed to identify taxa with common patterns of explained variance, with K = 8 based on the observation of the t-SNE point density. To identify which set of parameters drives the differentiation of a cluster, we compared the distributions of the explained variance of each parameter within the cluster using the Kruskal-Wallis and Wilcoxon paired tests (p < 0.
At the same time, this is an essential step for selectivity studies to advance and integrate AI methods in assessing the effectiveness of modified gears. We here conduct a bibliometric analysis to review the recent advances and applications of AI in automated tools for fish tracking, classification, and behavior recognition, highlighting how they may ultimately help improve gear selectivity. We further show how transforming external stimuli that influence fish behavior, such as sensory cues and gears as background, into interpretable features that models learn to distinguish remains challenging. By presenting the recent advances in AI on fish behavior applied to fishing gear improvements (e. g. , Long Short-Term Memory (LSTM), Generative Adversarial Network (GAN), coupled networks), we discuss the advances, potential and limits of AI to help meet the demands of fishing policies and sustainable goals, as scientists and developers continue to collaborate in building the database needed to train deep learning models.
Transfer learning for data-deficient environments We have shown that assessing fish behavior relies on analyzing trajectories. Considering tracks instead of detections generates even larger amounts of data than single detections of fishes on frames. Thousands to millions of such fish trajectories have likely been generated worldwide. These data may now be used to train models to detect fishes, at species level or as generic fish, in unseen environments. We provide a few examples of available published datasets that have been used to train models (Table  4 ). For tropical fishes, Fish4Knowledge (F4K;  Fisher et al. , 2016) , a project that started in 2010, garnered millions of images from GoPro cameras that were set-up in coral reef areas of Taiwan. The project resulted to 87K hours of video (95 TB) and 145 million fish identifications.
It has then made the successfully curated database available to the rest of the world and most of the developments in automatic classification and identification tools for fishes have used the database to train deep learning models (see in Table  4  uses of F4K:  Spampinato et al. , 2010; Palazzo and Murabito, 2014; Shafait et al. , 2016; Jalal et al. , 2020; Murugaiyan et al. , 2021). For temperate fishes, only a few commercial species can be automatically identified by existing models but are nonetheless gaining more recognition. Bonofiglio et al. (2022)  trained an AI pipeline to detect and track sablefish, Anoplopoma fimbria, in an underwater canyon in North America on ~650 hours of video recording with ~9000 manual annotations. Due to growing fish databases and application of image processing techniques, AI models can now detect fishes with human-like accuracy in some species such as Scythe butterfly fish  (Benson et al.
, 2013) , some tropical species  (Spampinato et al. , 2010) , and mesopelagic species  (Allken et al. , 2021a). Studying fish-gear interactions is particularly difficult due to the unique and challenging conditions often met at sea. Pipelines of automatic detections have applied transfer learning and data augmentation techniques to cope with the lack of available data. For example,  Knausgård et al. (2021)  applied transfer learning to train an AI system to identify temperate fishes that are commercially valuable, such as wrasses (Ctenolabrus rupestris, C. exoletus and Sympohodus melops) and gadoids (Gadus morhua, Pollachius virens, P. pollachius, Molva molva, and Melanogrammus aeglefinus). Using models pre-trained on available public datasets (see Table  4 , e. g. , Fish4Knowledge and ImageNet), they obtained high accuracies in object detection and classification using their fine-tuned models (86.
96% and 99. 27%, respectively). Transfer learning from preexisting object detection algorithms coupled with existing data from other environments can thus be a promising approach for the automatic analysis of fish species even from environments that still lack data  (Fisher et al. , 2016; Siddiqui et al. , 2018; Knausgård et al. , 2021) , additional augmentation methods, such as generating synthetic datasets, may help overcome the insufficiency of small datasets for training models  (Allken et al. , 2019; Villon et al. , 2021).
e. , Swarm Intelligence;  Ahmed and Glasgow, 2012, Boids algorithms; Alaliyat et al. , 2014). Algorithms to track behavior in congested human crowds have been developed based on motion capture and optical flow techniques  (Krausz and Bauckhage, 2011). Different types of human behavior can now be recognized by AI in all sorts of environment due to the considerable attention in the domain and since high performing models learn from a gigantic amount of training database of diverse human behavior (Popoola and  Wang, 2012; Vinicius et al. , 2013). Three-dimensional motion capture techniques can also provide more information such as depth and detailed tracking of animal paths  (Wu et al. , 2009). Moreover, 3D trajectories can provide the analytics (i. e. , positions, velocities, accelerations) to study cohesive and unique behaviors  (Sinhuber et al.
, 2016; Villon et al. , 2021). Complementary laboratory studies may also help collect consistent findings  (Hannah and Jones, 2012) , which are needed to gather a database of automatically classifiable behaviors. For example, the influence of light intensity on juvenile walleye pollock Theragra chalcogramma were studied in laboratory conditions and in situ and showed that juveniles either struck the nets more often or swam closer to them in darkness than at the highest illumination  (Olla et al. , 2000). Such Images systematic behavioral responses could thus be used to train an AI model which could then be used to automatically analyze replicates of additional trials. Similarly, AI applications would enable to amplify the number of replicates of sea or laboratory trials, for example when assessing how changes in the positions of stimuli influences species behaviors  (Larsen et al.
Existing and previous data mining and collection practices included outsourcing efforts. Fish4Knowledge branched out to volunteers, subprojects, and gamifying techniques  (Fisher et al. , 2016). Popular datasets such as ImageNet and COCO used Amazon Analytics to crowdsource annotations of objects  (Gauen et al. , 2017). McClure et al. (2020)  discussed that citizen science is beneficial for AI applied in ecological monitoring as it can fast track data collection since AI is now within reach because of integration in mobile devices and user-friendly platforms. The phytoplankton world is benefitting from citizen science as online portals are used by volunteers to do simple classification tasks that has led to millions of plankton ID's to be verified  (Robinson et al. , 2017). Moreover, scientists are adapting FAIR (Findability, Accessibility, Interoperability, and Reuse) data principles to realize the full value of fish behavior data and to carefully curate a unifying database  (Guidi et al.
, 2020; Bilodeau et al. , 2022). Bridging the gap between computer and marine sciences can accelerate the development of powerful tools for automated fish monitoring  (Goodwin et al. , 2021). User-friendly software platforms for image processing and analysis of animal tracks and events are publicly accessible and designed for non-AI experts  (Dawkins et al. , 2017). So even if observations of fish-gear interactions are more demanding in terms of observation requirements that can produce small sizes of data and are distinctly case-specific, training models can still be aided by means of data transfer, open-access databases, and participatory platforms. This will be beneficial for everyone as endtools that grow in performance will also grow in scalability thanks to shared data. If there are enough collaborations across domains, extensive engagement with fish ethologists to construct behavioral classifiers, consistent sharing of reproducible, understandable, and scalable data then it might become possible to quantify, in near completeness, what a fish is doing or how it is interacting with its environment in any conditions.
Acknowledgments The authors are thankful to  Abdelbadie Belmouhcine  for his valuable insights on deep learning methods, to  Sonia Mehault ,  Julien Simon  and  Pascal Larnaud  for the discussion on gear selectivity, to  Marie Savina-Rolland  for her comments on the manuscript and to  Megan Quimbre  ( IFREMER, Bibliothèque La Peŕouse ) for the bibliometric analysis. The authors are also grateful to the two reviewers for their time and criticisms which greatly improved the manuscript.
73 grazing water light (Haldex Sports (RGB videos) 2021b Pre-trained ResNet50 flow data algorithm for optical + Motion estimation free-swimming patches sea grass tricuspidata) on luderick (Girella and 0. 79 (without between 0. 84 and 0. 87 precision, F1 score filtering); Recall, spatiotemporal Action Cam HD 1080p) (with spatiotemporal filtering) YOLOv3 + Quantify Precision of 0. 897, a Separate Fish tank 120 Go-pro Hero 7 Own dataset Hu et al. , MobileNetv2 feeding and recall of 0. 884, an feeding and light- Black (RGB videos) 2021 backbone + stress behavior intersection over hypoxia emitting improved detection of carps union of 0. 892 experimental diodes method with (Carassius conditions pyramid pooling auratus) and block and multiscale catfish feature extraction (Pelteobagrus technique fulvidraco) Characterized Identification accuracy Fighting Lab light Video camera - Own dataset Laan et al.
, Idtracker. ai hybrid mutual motor of 0. 98 behavior intensity not specified 2018 system (Gaussian coordination of 200- mixture model + and multi- 300 lx at greedy acceleration functional the water minimization maneuvers in surface principle) zebrafish (Dania rerio) Behavior of Accuracy for behavior Construction, Lab Artificial RaspberryPi Own dataset Long et al. , cichlids recognition of feeding, light camera v2 (RGB videos) 2020 3D Residual (foraging, construction behavior mating source Networks construction, of 0. 76 and social behavior) Characterizing Detection F1 score of Tracking In situ rocky Natural Action cameras Own dataset Lopez- movement 0. 91, 120 of 169 angles: (rocky reefs light (1080p Haldex (RGB videos) Marcano Mask RCNN + 3 behavior of individuals correctly and seagrass Sports Action et al.
, 2021 different trackers yellowfin identified, 0. 78 meadows) Cam HD) (MOSSE, Seq-NMS, seabreams tracking accuracy SiamMask) (Acanthopagrus (MOSSE and australis) SiamMask) and 0. 84 (Seq-NMS) Dual-Stream Tracking if Behavior prediction of Feeding/Non Breeding Natural Video camera Own dataset Måløy et al. , Recurrent Network feeding 0. 80 feeding cages at sea light (not specified) (RGB videos) 2019 (DSRN) (Spatial behavior of Network + 3D CNN salmon (Salmo + LSTM) salar) Identifying Average precision of Startle/non- In situ, open Natural Fixed in situ Own dataset McIntosh networks YOLOv3 + LSTM (Anoplopoma in sablefish startle behavior 0. 85 on video clips startle event tropical water, light camera (RGB videos) et al. , 2020 fimbria) (Continued).
inf-cv. uni-jena. de/fine_ ~700 images of 12 different fish Mixed Species Adriatic Sea in Jäger et al. , Okafor et al. , 2018; Qiu et al. , 2018; Images Dataset grained_recognition. html#datasets species in real life conditions Croatia 2015 Zhao et al. , 2018; Guo et al. , 2020; (currently not accessible) (120 images in training set and Yan et al. , 2021; Pang et al. , 2022 674 in testing set) DeepFish https://github. com/alzayats/ ~40,000 annotated classification Mixed in situ Fish/no Coastal marine Saleh et al. , Laradji et al. , 2020; Saleh et al. , 2020 Images DeepFish labels, collected from 20 fish environments in 2020 different habitats Australia Deep Vision http://metadata. nmdc. no/metadata- Two surveys 2017 to 2019 from Blue whiting, Atlantic herring, Atlantic Species In situ from Allken et al.
, Allken et al. , 2021b Images Fish Dataset api/landingpage/ Deep Vision system herring, other mesopelagic fishes surveys + 2021b 01d102345aef4639f063a13ea20cd3f3 synthetic data FathomNET http://fathomnet. org ~80,000 images of marine Mixed Mixed Worldwide Boulais et al. , Katija et al. , 2021b Images and animals, 106 000 localizations, 2020; Katija Videos 26 000 h videos, 6. 8 million et al. , 2021a annotations, 4 349 terms Fish4Knowledge http://www. perceivelab. com/ 3. 5k bounding fishes/700k 10- Species of tropical fishes Fish/no Taiwan coral (F4K) datasets minute video clips fish reefs.
Furthermore, we show that phonetic trills are less frequent than phonetic taps, while phonemic trills are more frequent than phonemic taps. As a consequence, inferring the presence of trilling in a sound system from the presence of r in its transcription is not as straightforward as one might expect. These findings highlight the critical importance of being absolutely explicit about the meaning of the various symbols found in grammars and secondary databases, and of r in particular, as a preliminary step in a broad range of studies, including cross-linguistic comparisons, inferences about the past or generalizations about language acquisition and articulatory effort.
We then describe trills and taps from phonetic and cross-linguistic perspectives. These are followed by a detailed description of our dataset (the Illustrations of the IPA, hereafter Illustrations), the data collection strategy, the coding and counting of the trills and taps, and the description of the two types of narrative transcription (phonetic and phonemic) in Illustrations that were used to analyze the use of r and ɾ. We then present the general results of our data collection with regard to the year of publication, the available informant characteristics, as well as the macroarea, language family and speaker population size of the different varieties covered. We further present the results of our main study on the use of the r and ɾ in Illustrations, showing that there is more variability in the transcriptions than expected given the existing literature and 'received wisdom'.
We also show that /r/ is more frequent in transcriptions compared to  [r] , while /ɾ/ is conversely less frequent in transcription compared to  [ɾ]. Finally, in the Section 5, 'Discussion and conclusions', we suggest that care should be taken when taking the symbol r at face value in grammars, databases and other linguistic resources without further clarification. As a corollary, our findings might signal the need to re-evaluate already published research concerning the frequency of the alveolar trill within and between languages  (Maddieson 1984) , its areal and genealogical patterning, and the forces influencing these patterns (Moran, Lester & Grossman 2021), its acquisition  (McLeod & Crowe 2018 , Stemberger & Bernhardt 2018)  and its extra-linguistic associations  (Winter et al. 2022) , among others.
A very short contact, similar in duration to one cycle of the vibration of a trill, is called a tap, such as the [ɾ] in Spanish pero 'but'. (IPA 1999: 8)  Note: most forms of English, French, German, Swedish do not have trills except in over-articulated speech, for instance when trying to be clear over a poor telephone line. (IPA 1999: 19)  In this edition, there is no explicit guideline on using r as a generic symbol to denote different phones, but there is no indication to avoid this practice, either. In conclusion, the IPA mandates a generic use of the r symbol that blurs what was actually analyzed: while it conveys the broad idea that this is a rhotic, it is left to the author(s) to somehow clarify the place(s) and manner(s) used. This imprecision, unfortunately, can be quite misleading for typological, comparative work, especially when large databases and quantitative methods are used (but even more focused, manual comparisons might be affected as well).
The large variation observed in the phonemic trill may be in part explained by the fact that allophonic trills are articulatorily more complex than allophonic taps (the latter being acquired earlier than the former -McLeod & Crowe 2018), require more energy and a precise control of the parameters involved in its aerodynamics, which can lead to trilling failure. Therefore, it seems that more than a century after the publications of the first international guidelines on phonetic transcription, the way the symbol r is used throughout the linguistic community is still not consistent and universally shared. We argue here that this situation is highly detrimental and significantly hinders the capacity to develop a typology of the [r] sound and of the rhotics from a phonetic perspective. Answering questions such as whether or not [r] is rare becomes unnecessarily complicated, and querying databases needs extra care to identify and control for the noise induced by the (sometimes implicit) use of different guidelines and expectations concerning the way sounds should be represented.
Data collection The primary data consists of all the relevant information extracted from illustrations available on the JIPA website. Due to a change of style after 2000, when Cambridge University took over the production of JIPA (IPA 2000), we split them into those published before 2000 (42 illustrations), and those published between 2000 and 2020 (168 illustrations). We checked the consistency of the pre-2000 illustrations with their re-edited version in the Handbook of the International Phonetic Alphabet: A guide to the use of the International Phonetic Alphabet in 1999 (IPA 1999), and we included two additional illustrations present only in this source but absent from the JIPA website (American English by Ladefoged 1999: 41-44 and Portuguese (European) by Cruz-Ferreira 1999), resulting in a total of 213 illustrations, including 46 published before 2000.
In some cases, we looked at the orthographic transcription that was provided by the authors to give us clues about <r> segments that could be omitted or realized differently from what was expected. When several transcriptions are provided, we have used the highest number n R across the different transcriptions. An additional logical variable denoting the presence or absence of <r> in the orthographic system of the language or in the transliteration was added if available. For example, in some illustrations where the language described uses a Cyrillic alphabet, we find in the transliterations <r> standing for <p>, in which case we consider that the language does contain <r> in its orthography. Although this data was collected by a single coder (RA), this was done in several separate 'rounds', which enabled quality checking and, if necessary, correction of earlier rounds in subsequent later rounds.
Counting all segments n Seg Finally, in order to normalize the number of occurrences of rhotics across the illustrations, we also counted the total number of segments in each transcription. Although all the transcriptions come from Illustrations, there were inconsistencies in the format in which we recovered them: • some transcriptions are available in text format (. txt) from  Baird, Evans & Greenhill (2022)  (available at https://github. com/SimonGreenhill/jipa) • when possible, transcriptions were copy-pasted from the PDF of the illustrations or from the Illustrations web page hosted by the JIPA • some transcriptions from which copy-pasting was not possible were submitted to Optical character recognition (OCR) using the R package tesseract  (Ooms 2021 ) and corrected for potential errors or were manually typed If there was more than one transcription in an illustration (for example, one phonemic and one phonetic transcription, or for different varieties), we counted the number of segments in the broadest transcription available, or by averaging the counts found in the transcriptions when they had the same level of phonetic precision.
(Sebregts 2014: 157)  As explained in this paper, we consider that this ambiguity results from several factors that led the linguistic community to tolerate a blind spot which mirrors the difficulty of handling rhotics in general and trills in particular. We consequently suggest that clarifying the guidelines on rhotics would improve the gold standard against which Illustrations is published as already proposed by  Whitley (2003: 84)  by '[a]dopt[ing] the macron for specifying a trill [ie. r# ], leaving plain r entirely free for its widely understood value of "any rhotic"'. Beyond this aspect, we would like to emphasize that the illustrations are also integrated 'as they are' in secondary sources such as phonological databases. There has indeed been a recent explosion in the availability and use of such databases  (Moran & McCloy 2019 , Mortensen et al.
2020) , and in their relevance to the exploration of large-scale typological and historical questions. Such databases usually compile data from different sources, among which we can find papers published in JIPA (as, for example, in PHOIBLE and ALLOVERA). Irrespective of their intended nature (phonemic, allophonic or phonetic), they make use of the symbols proposed by transcription systems, and the symbols of the International Phonetic Alphabet (IPA) are omnipresent and currently almost universally used in linguistics. While they are arguably robust enough for large-scale studies concerning broad phonemic classes and their distribution across language families and geographical areas, our findings highlight that taking them at face value might not be sufficient for research aiming at uncovering subtle effects involving actual acoustic and/or articulatory features (such as those involving sound symbolism, the acoustic adaptation to the environment or weak effects of normal variation in the anatomy of the vocal tract).
Acknowledgements We wish to thank  Ioana Chitoran ,  Bob Ladd  and  James Kirby  for feedback on earlier versions of the manuscript. We are grateful to the JIPA Associate Editor  Alexei Kochetov  and the reviewers for their suggestions and comments, and  Jayden Macklin-Cordes  for proofreading the manuscript. The authors RA and DD were funded by the  IDEXLYON Fellowship  Grant  16-IDEX-0005. The authors are grateful to the  ASLAN  project ( ANR-10-LABX-0081 ) of the  University de Lyon , for its financial support within the  French program 'Investments for the Future ' operated by the  French National Research Agency (ANR).
Ph. D. dissertation, MIT. Lindau, Mona. 1985. The story of /r/. In Victoria A. Fromkin (ed. ), Phonetic linguistics: Essays in honor of  Peter Ladefoged , 157-168. Orlando, FL: Academic Press. Maddieson ,  Ian. 1984. Patterns of sounds. Cambridge:  Cambridge University Press. Maddieson ,  Ian. ,  Sébastien Flavier ,  Egidio Marsico ,  Christophe Coupé  &  FranCois Pellegrino. 2013. LAPSyD : Lyon--Albuquerque phonological systems database. In International Speech Communications Association (ed. ), Proceedings of the 14th Annual Conference of the International Speech Communication Association (INTERSPEECH 2013), Lyon, 3022-3026. Magnuson ,  Thomas Judd. 2007. The story of /r/ in two vocal tracts. In Jūrgan Trouvain &  William J. Barry  (eds. ), Proceedings of the 16th International  Congress of the Phonetic Sciences  ( ICPhS XVI ), Saarbru_ cken, 1193-1196.