dataesr/scientific-paragraphs-categorization huggingface dataset:
Datasets Hydrological and phytoplankton samples were collected at high tide at coastal station "C" (50 • 40 75 N; 1 • 31 17 E, 20-25 m maximum depth), which belongs to the French monitoring network SOMLIT 1. Sampling was carried out on an average of every 3 weeks from February 2007 to December 2013. Subsurface (2 m depth) temperature (SST, • C) and salinity (S) were measured with a conductivity temperature depth (CTD) Seabird profiler equipped with photosynthetically active radiation (PAR, E m -2 d -1 ) sensor (QSP 2300; Biospherical Instruments Inc. ). Dissolved inorganic phosphorous (DIP, µM), inorganic silica (DSi, µM), and inorganic nitrogen [the sum of nitrates, nitrites, and ammonium; (DIN, µM)], total suspended matter (TSM, mg L -1 ), chlorophyll a (Chla, µg L -1 ), particulate organic carbon (POC, µgC L -1 ), phytoplankton, and protozooplankton were measured by means of a Niskin Water Sampler (8 L).
Phytoplankton and Copepod Species Traits Information on resource traits is scarce in the literature for most phytoplankton species  (Litchman and Klausmeier, 2008; Breton et al. , 2017). Consequently, an a posteriori approach Competitive ability under nutrient limitation, silica requirement  Grover, 1989; Karp-Boss et al. , 1996; Leynaert et al. , 2004; Takabayashi et al. , 2006; Musielak et al. , 2009; Lovecchio et al. , 2019  Coloniality Ordinal (R and D) 0: No, 1: <5 cells, 2:  [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] , 3:[20, 100], 4:>100 Competitive ability under nutrient limitation  Grover, 1989; Karp-Boss et al. , 1996; Takabayashi et al. , 2006; Musielak et al. , 2009; Bjaerke et al. ,   to defining resource use and requirement traits was used. For each species, biovolume (V, µm 3 ), maximum linear dimension (MLD, µm), and the surface to biovolume cell ratio (S/V ratio) were calculated from the median values of a series of microscopic measures over the entire 1996-2019 diatom datasets acquired at Station "C" (Breton unpublished data).
5: mixed feeding) were selected, as they directly/indirectly influence species fitness through growth, reproduction, and/or survival  (Litchman et al. , 2013; Kiørboe and Hirst, 2014). Most of them are responsive to changes in environmental conditions  (McGinty et al. , 2018). Data were obtained from the copepod trait database constructed by  Benedetti et al. (2016)  and completed by  Brun et al. (2017) , and references therein. For each copepod species, all available information on species size was compiled, and the maximum size value as representative of adult species was kept, note that sexual dimorphism was not considered. The degree of herbivory (carnivory, detritivory, or omnivory) was coded following  Mondy and Usseglio-Polatera (2014). In this study, a coefficient of (i) 1/3 for omnivore; (ii) 2/3 for omnivoreherbivory (dominance of herbivory in copepod diet) or 2/3 for omnivore-carnivory (dominance of carnivory in copepod diet), with 1/4 for the two other diet categories; and (iii) 1 for species with carnivore/herbivore/detritivore diets, and 0 for the two other diet categories (refer Chihoub et al.
e. , observed) diatom community and functional values obtained from a random community: for each sampling date, trait data were randomized 999 times ("permatfull" in the "vegan" package), while maintaining species richness and abundance constant. SES was calculated as follows: SES = x obs -xran σ ran (1) where x obs is the observed values of the functional metric at a given date; xran is the mean, and σ ran is the SD of the FD of randomly assembled communities. Assuming a normal distribution of the random communities, the traits derived from the sampling dates falling into the 95th (or higher) percentile of the random distribution were considered "over-dispersed" (i. e. , niche differentiation being the major process driving community assembly) and trait falling into the 5th (or lower) percentile were considered "under-dispersed" (i.
Hypervolume estimates the niche breadth of one or several dimensions from the calculation of uni or multivariate kernel density. This method captures irregular shapes and does not require a specific density function, unlike OMI analysis that requires normal distribution: estimation of the hypervolume is, therefore, more accurate when high-dimensional or holey datasets are considered. An accurate estimate of kernel density requires high amounts of trait/environmental data, which rises as dimensionality increases, however (e. g. , about >67 and >770 for three and five dimensions, respectively,  Silverman, 1986). For a comprehensive assessment of environmental gradients, the calculation was, therefore, based on the ecological specialization of long-term diatom and copepods datasets, collected between 1996 and 2019 at the station "C, " and from 2001 to 2017, respectively, in the downstream part of the Gironde estuary  (Breton et al.
Exploratory Analyses First, box plots (Step 4, Supplementary Figure  1 ) were computed combined with a locally weighted scatterplot smoothing (LOESS;  Jacoby, 2000)  curve in association with a 95% confidence interval (function "geom_smooth, " package "ggplot2, "  Wickham, 2016)  to depict the seasonal variations. Monthly differences were assessed by a Kruskal-Wallis test, followed by a post hoc Nemenyi test  (Legendre and Legendre, 1998; package "PMCMRplus, " Pohlert, 2014). To explore and summarize seasonal changes in the abiotic environment, a principal component analysis (PCA) was performed with the "ade4" package. To explore seasonal variations in both the mean values of key biological traits and in the degree of ecological specialization of the diatom and copepod communities, a PCoA (Step 4, Supplementary Figure  1 ) was computed on a community dissimilarity matrix (Gower distance) using monthly data of niche breadth and CWMs of different functional attributes.
As for the COIA, variables that attest to the hydrometeorological and local environmental conditions (abiotic and prey biomass) were selected backward to obtain the lowest residual proportion. To explore the effect of Phaeocystis blooms on species richness, functional structure, ecological specialization, and FD of the diatom community, a Kruskal-Wallis test was performed using different classes of Phaeocystis biomass [1: <100; 2: (100, 500); 3: (500, 1000); 4: >1,000 µgC L -1 ] coming from the 1996 to 2019 dataset (refer "Ecological specialization").
ACKNOWLEDGMENTS We acknowledge the three reviewers for their constructive comments, which allowed us to greatly improve the manuscript. We thank the crews who helped during long-term monitoring downstream of the Gironde estuary (2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017) and in coastal waters of the eastern English Channel , and the SOMLIT teams for sampling and physicochemical measurements and analysis, especially  N. Degros ,  E. Lecuyer , and  H. Derriennic. We also thank the students  T. Rault  and  G. Parmentier  for copepod identification and counts and  S. Bosc  and  P. Faye  for their contribution to diatom cell measurements. This work is dedicated to  J. -M. Dewarumez , who managed  SOMLIT at Wimereux , and  P. Lebleu , who performed sampling in the Gironde estuary.
The Transformation Function Diag-Skills' reasoning is based on mass distributions. It uses these masses in order to determine the state of the learner's skills. However, as indicated in the architecture, the diagnosis module receives numerical grades from the evaluation module. These numerical values are not directly processable by Diag-Skills and need to be transformed into a distribution of belief masses. To do so, we define the transformation function Τ(, , ). The first parameter () refers to the score obtained by the learner in a given assessment. It is defined in the range [0, ], where 0 and  represent respectively the minimum and maximum score that the learner can get in an assessment. The second parameter () represents the minimum score required to validate the assessment. Finally, () represents a range of doubt on.
The Transformation Function Diag-Skills' reasoning is based on mass distributions. It uses these masses in order to determine the state of the learner's skills. However, as indicated in the architecture, the diagnosis module receives numerical grades from the evaluation module. These numerical values are not directly processable by Diag-Skills and need to be transformed into a distribution of belief masses. To do so, we define the transformation function T(ne, E, ). The first parameter (ne) refers to the score obtained by the learner in a given assessment. It is defined in the range [0, n], where 0 and n represent respectively the minimum and maximum score that the learner can get in an assessment. The second parameter (E) represents the minimum score required to validate the assessment. When a skill is evaluated, Diag-Skills receives an evaluation grade.
Implementation Diag-Skills is developed in Python. It uses Pandas library to manipulate datasets. At this stage, the current version of Diag-Skills does not have a graphical user interface. It runs online on Google Colab Platfrom. The main function of the program takes as input a csv file that contains the evaluation scores of the students for each skill (cf. Table  1 ), and returns a csv file that includes the skills' state after the diagnosis process (cf. Table  2 ).
Discussion and Conclusions As presented in the previous section, Diag-Skills showed a good prediction accuracy in the two studies. We noticed a better precision when the predictions were based on both top-down and bottom-up propagation (85% precision in the first experiment and 92% precision in the second experiment). Furthermore, we investigated the prediction when there were no effective evaluations to rely on by comparing Diag-Skills' predictions with the instructor's predictions. We added to these the cases of the first experiment where Diag-Skills failed to predict the correct state. The results showed that in this case, the module would at least make the same predictions as the instructor, as the Kappa measure showed an almost perfect agreement. This result suggests that Diag-Skills is capable of replicating human diagnosis with a high level of agreement.
e. replicate). Chambers, containing 10 ml of filtered seawater at the corresponding salinity, were then closed, ensuring the absence of any air bubbles within the chamber. Measurements were carried out at the corresponding temperature using a four-channel fiber optic oxygen meter (Firesting, Pyro-Science GmbH). All measurements started in fully oxygenated water and oxygen concentration was registered each 10 s through the Pyro Oxygen Logger software as a function of declining O 2 partial pressure (pO 2 ). The first 20 min of each measurement were discarded to reduce the impact of animal hadling on measurements. Four measurements were recorded in parallel, in all cases one being a blank (containing no animals and serving for determining background (microbial) respiration). A total of 8 replicates were carried out for each species and experimental treatment to compare metabolic energetic expenditure.
Data analysis To determine the interspecific responses of the two species to the different treatments, we used a three-way univariate PERMANOVA with species, temperature and salinity as fixed factors. Additionally, to explore a possible gender effect in their intraspecific response, we use a three-way univariate PERMANOVA with temperature, salinity and sex as fixed factors for each species. Where PERMANOVA indicated a significant difference for a given factor, the source of the difference was identified using Pair-wise tests. The influence of sex was tested for all parameters except for respirometry responses (RR, RI and P crit ) since these parameters were measured using animal pools (each replicate consisting in 5 males and 5 females). Analyses were based on Euclidean distances matrices and statistical significance (P < 0.
05) was tested using 9999 permutations under a reduced model. When the number or unique permutations performed was low, we use the Monte Carlo Pvalue instead the permutation P-value  (Anderson, 2001). To estimate the P crit we used both the segmented, or nonlinear 'brokenline' regression approach  (Muggeo, 2003) , and the 'broken-stick' regression method  (Yeager and Ultsch, 1989) , available in R package "RespR"  (Harianto et al. , 2019). We also calculated the RI using the R package "MESS"  (Ekstrøm, 2016) , following the procedure implemented by  Tremblay et al. (2020). They define the respiration strategies using median and quartile RI values as: low degree of oxyregulation (0. 25 < RI median < 0. 5); high degree of oxyregulation (0. 5 < RI median < 1; Q1 > 0. 25 or Q3 > 0. 75); oxyconformity (RI median ~ 0; Q1 < 0 and Q3 > 0) and metabolic suppression (RI median, Q1 and Q3 < 0).
Acknowledgments We are extremely grateful to  Montserrat Sole  for her kind help during field works. We sincerely thank the  Port St. Clair staff (Sète, France)  for allowing us to sample. Financial support for this study was provided by the  Ministerio de Ciencia, Innovación y Universidades  (Project  CGL2017-82739-P  co-financed by the  Agencia Estatal de Investigación-AEI  and  Fondo Europeo de Desarrollo Regional-FEDER ). MR was funded by a  Juan de la Cierva Postdoctoral Fellowship  ( IJCI-2017-34656 ). GRI was funded by  Marie Curie Actions FP7-PEOPLE-2013-IEF  (grant number  622087  -" IAS-Life ").
Multitemporal environmental and urban studies are essential to guide policy making to ultimately improve human wellbeing in the Global South. Land-cover products derived from historical aerial orthomosaics acquired decades ago can provide important evidence to inform longterm studies. To reduce the manual labelling effort by human experts and to scale to large, meaningful regions, we investigate in this study how domain adaptation techniques and deep learning can help to efficiently map land cover in Central Africa. We propose and evaluate a methodology that is based on unsupervised adaptation to reduce the cost of generating reference data for several cities and across different dates. We present the first application of domain adaptation based on fully convolutional networks for semantic segmentation of a dataset of historical panchromatic orthomosaics for landcover generation for two focus cities Goma-Gisenyi and Bukavu.
Introduction Remote sensing datasets provide the capability to map land-cover which is useful for environmental and urban-related studies  [1, 2]. A set of remote sensing data collected over a, possibly large, window of time enables to study the historic evolution of the landscape. Thus, multitemporal studies often must make use of both recent imagery and historical datasets. In the mid-20th century, panchromatic aerial photographs were the main source of high spatial resolution imagery  [3]. During the colonial era in Africa, the authorities conducted multiple air survey campaigns and generated topographical maps at different scales. The availability and completeness of these datasets has been affected by several factors such as the political course of a country after independence, and the administration of the relevant surveying departments.
The availability of archives of historical orthomosaics provides the capability to reconstruct the historical urban developments of these cities. This is a challenging task especially since some of the widely used global datasets only ISPRS Int. J. Geo-Inf. 2021, 10, 523 2 of 19 start from the 1970s and have coarse spatial resolutions  [4]. With the rapid population growth rates, it is projected that over 22% of the world's urban population will be residing in Africa by the year 2050, creating implications on infrastructure, demand for housing and environmental degradation  [5]. The sustainable development goals (SDGs) are a set of 17 targets that provide a pathway towards a peaceful and prosperous planet by the year 2030 for both the developed and developing countries  [6]. Most of the issues it seeks to address exhibit adverse effects that are imbalanced mostly towards countries in the Global South.
Both methods aim at the generation of features that are both invariant and yet discriminative in the source and target domain. The rationale behind the method's selection is they have the advantage of an easy integration into a deep learning framework, as well as avoiding distortion of data at the raw pixel level space leading to an improved generalisation power at test time  [25]. The first approach is the domain adversarial neural network (DANN), which uses an adversarial loss function that aims at predicting domain invariant labels  [26]  with an example of a binary land-cover application using multi-date and multi-site Landsat satellite imagery  [27]  and multi-class classification from multispectral and hyperspectral datasets  [28]. The second approach is the D-CORAL that reduces the domain shift using the second-order statistics  [29]  with an example of a different task of object-detection of vehicles from RGB aerial images  [30].
In addition, we conduct fine-tuning experiments and evaluate the amount of additional reference data needed from the target domain. To the best of our knowledge, it is the first application of domain adaptation that uses a dataset of historical panchromatic orthomosaics. Furthermore, most of the existing works in the remote sensing domain have been conducted using multi-spectral or hyperspectral images, and largely for land-use classification  [21, 28]. In summary, the objectives of this study are the following: (i) to evaluate and compare two unsupervised domain adaptation methods for the task of land-cover mapping from two historical datasets of panchromatic orthomosaics from the 1940s and 1950s, and (ii) to assess the added benefits of fine-tuning the domain adaptation networks using small amounts of data from the target domain.
Data Description In this work, we use a dataset of historical panchromatic orthomosaics generated from the collections of aerial photographs of the Belgian Royal Museum for Central Africa (see In this work, we use a dataset of historical panchromatic orthomosaics generated from the collections of aerial photographs of the Belgian Royal Museum for Central Africa (see Appendix A). It comprises of images from the city of Goma-Gisenyi (whereby Goma is in the Democratic Republic of Congo and Gisenyi is in Rwanda) captured in 1947, and the city of Bukavu in the Democratic Republic of Congo, captured in 1959 (Figure  1 ). These orthomosaics are based on scanned paper reproductions of aerial surveys that were carried out in the 1940s and 1950s in Central Africa. The image pre-processing and photogrammetric processing to obtain the orthomosaics is described in Appendix A.
A shift in the label/semantic space exists that makes the images structurally different, for instance, the building class appears different between the two images. In addition, the mixed bare ground and low vegetation classes are diverse across both images which can be attributed to differences in lithology of the two cities whereby soils in Goma are black while those in Bukavu are red  [35, 36]. This combination of several domains shifts makes this task challenging. The orthomosaics are panchromatic and have a single channel which implies there is limited spectral information for the discrimination of classes. Therefore, any changes to the magnitudes and distribution of the grey values can affect the discrimination ability of There are several significant sources of domain shift between these two datasets.
Experimental Set-Up The first set of experiments is conducted using standard U-Net without domain adaptation. Training on data from only the source domain provides the lower bound of test accuracy on the target domain (source only). On the other hand, training on images from the target domain provides the upper bound of the test accuracy on the target domain (target only). In both domain adaptation strategies, data from the source domain includes the raw images and the corresponding labels, while only the raw images are available for the target domain. Figure  5  shows the location of spatially distributed tiles from which training and testing samples were generated for both Goma-Gisenyi and Bukavu. In total, a dataset of 3000 samples of patch size 128 × 128 pixels is obtained from the tiles designated for training and the distribution is shown in Figure  6.
Experimental Set-Up The first set of experiments is conducted using standard U-Net without domain adaptation. Training on data from only the source domain provides the lower bound of test accuracy on the target domain (source only). On the other hand, training on images from the target domain provides the upper bound of the test accuracy on the target domain (target only). In both domain adaptation strategies, data from the source domain includes the raw images and the corresponding labels, while only the raw images are available for the target domain. Figure  5  shows the location of spatially distributed tiles from which training and testing samples were generated for both Goma-Gisenyi and Bukavu. In total, a dataset of 3,000 samples of patch size 128 × 128 pixels is obtained from the tiles designated for training and the distribution is shown in Figure  6.
The networks are trained in 50 epochs, using ADAM optimizer  [40]  with a learning rate of 0. 001, and implemented using python 3. 7 and PyTorch deep learning framework. The choice of number of epochs, optimizer and learning rate has been identified as optimal after several trial-and-error tests. We use a computer equipped with an NVIDIA GPU GeForce GTX 1080 with 8 GB of RAM. We further conduct experiments to evaluate the amount of training samples from the target domain needed to fine-tune a domain adaptation network. This is accomplished to assess the added benefit of domain adaptation in reducing amount of reference labels required in a target domain. For this, a domain adaptation network trained using a combination of 150 labelled samples from the source domain and 150 unlabelled samples from the target domain is fine-tuned using samples from the target domain of three sizes namely 150, 300 and 450 selected from the original sample set of 3000 samples, and the accuracy metrics averaged over five runs.
Discussion In this paper, two real-world image datasets from two different cities of Goma-Gisenyi and Bukavu, acquired in 1947 and 1959, respectively, have been utilized. From the experiments, by looking at both the numerical and qualitative results, it is evident that relying exclusively on unsupervised domain adaptation might not always be sufficient to guarantee a certain level of performance in classification accuracy. In Bejiga et al. [27] , by using a multi-date, multi-site and multi-spectral dataset, it is observed that the use of domain adaptation for a problem with more than one source of domain shift (i. e. , images of different spatial locations and acquired at different time periods) increases the level of challenge in unsupervised domain adaptation approaches, affecting their performance.
Semantic segmentation of multiple land-cover classes from remote sensing imagery is more challenging than a scene classification because of the variety of categories within a scene. Furthermore, the use of historical panchromatic orthomosaics, evident in this work, complicates the discrimination of classes because of the limited spectral information available. Lastly, due to the quality of the historical orthomosaics used in this work, another possible research direction would to be to investigate effectiveness of the methods on a dataset of higher quality.