dataesr/scientific-paragraphs-categorization huggingface dataset:
g. ,  Nathan et al. , 2012; Carroll et al. , 2014; Ladds et al. , 2016). Preliminary model selection and hyperparameter tuning for each classifier type were performed using a Bayesian Optimization approach with 5-fold cross-validation. We followed the workflow described in the associated documentation (https://fr. mathworks. com/help/stats/bayesian-optimization-workflow). With the Statistics and Machine Learning toolbox's Bayesian Optimization algorithm, we optimized the different types of supervised machine learning algorithms across a selection of classification models and hyperparameter values in order to pre-select the best classification models that were suitable for our dataset. An optimization run pre-selected one model as the best one by seeking to minimize classification error. For Ensemble classifiers, the selection of classification models included Bootstrap Aggregation (Bagging), Random Forest (Bag), Random Subspace (Subspace), Adaptive Boosting for Binary Classification (AdaBoostM1), Adaptive Boosting for Multiclass Classification (AdaBoostM2), Gentle Adaptive Boosting (GentleBoost), Adaptive Logistic Regression (LogitBoost), Linear Programming Boosting (LPBoost), Least-Squares Boosting (LSBoost), Robust Boosting (RobustBoost), Random Undersampling Boosting (RUSBoost), and Totally Corrective Boosting (TotalBoost).
For KNN classifiers, it included Cityblock, Chebyshev, Correlation, Cosine, Euclidean, Hamming, Jaccard, Mahalanobis, Minkowski, Seuclidean, and Spearman metrics-based KNN. For Decision trees, it included classifiers that are based on Gini's diversity index (Gdi), twoing rule, and deviance splitting criterion. For SVM, it included SVM with Gaussian, linear, and polynomial kernels. These different models are detailed in the aforementioned documentation. The number of runs we used for the data splitting (30 runs) and for the Bayesian optimization (100 runs per split per classifier type) were chosen to optimize results stability and processing speed. For each trial and each class, we calculated five evaluation metrics for making a final decision on the classification model to be retained: Sensitivity (True Positive Rate, Hit Rate or Recall), Precision (Positive Predictive Value), False Positive Rate (FPR), F -score, and Global accuracy.
Generalization across individuals To assess whether the model can generalize across different individual whale calves, we also tested a leave-one-out split, similar to  Ladds et al. (2016). We trained the model with the highest performance on two calves' data. We then tested its performance on the unseen remaining calf data, establishing if the tag data obtained from individuals could be used to identify the suckling behavior of a different individual. We repeated this analysis three times, leaving out a different calf's data each time following the same machine learning workflow outlined in Fig. S1. Aside from possible influence of training set size (e. g. , the recorded data from the individuals used for training is relatively small), lack of generalization ability across individuals may result from a dataset with high inter-individual variability of suckling behavior characteristics.
To address this potential issue, we tested whether excluding features that contribute most to inter-individual difference (if any) in the suckling blocks would improve the performance of the supervised machine learning. To identify the features that contribute most to inter-individual difference, we trained a Random Forest algorithm to classify individuals based only on the suckling blocks using the R software package randomForest  (Liaw & Wiener, 2002). Features that contributed the most to the differentiation by individual were identified as those that had a high Gini index in the Random Forest. We re-ran the leave-one-out design, as described above, but excluding the features identified by using the Gini index. We confirmed the reduction of the interindividual difference of the suckling blocks by examining the error rate (Out-of-the-bag error rate) of the Random Forest algorithm trained to classify individuals.
Tag deployments Four calves were tagged with CATS cam tag in the Sainte Marie channel: one during the calving season of 2018 (identified as Calf1) and three during the calving season of 2019 (identified as Calf2, Calf3, and Calf4). Calf1 and Calf2 were tagged with Cats1 while Calf3 and Calf4 were tagged with Cats2. Their immediate reaction to tagging was a slow evasive swimming, which corresponded to a mild reaction  (Stimpert et al. , 2012). All of them had unfurled dorsal fins, indicating that they were not neonate calves  (Cartwright & Sullivan, 2009). The deployment on Calf4 was not suitable for our study as the tag (thus the camera) was pointing toward the side of the calf (i. e. , away from the mouth) and was thus excluded from our analyses. For Calf3, the data towards the end of the deployment was not usable and thus not analyzed due to lack of visibility on the corresponding video recording as the night approached.
Best classifier for automatic identification of suckling A total of 7,827 behavioral blocks of 2 s duration (4,697 for training and 3,130 for testing, 60:40 holdout splitting) were obtained from the datasets after removing blocks too close to the surface and those of high speed activities (see Segmentation in the Method section). The raw data used for this part of the study are available in Data S2. No suckling events were affected by our thresholds for removing blocks too close to the surface and those of high speed activities. The class 'suckling' represented 4% of the data and the class 'non-suckling' represented 96%. Figure  3A  presents all models pre-selected by the optimization procedure for identifying automatically suckling in non-labelled data. How often each model was selected over the 30 runs of the optimization procedure for each type of classifier is presented in Fig.
Generalization of the supervised machine learning across individuals We evaluated the generalization ability of the AdaBoostM1 model, the best identified classifier in the context of suckling identification, using a leave-one-out design. The goal was to assess whether we can use data from other individuals to detect the suckling behavior of a new individual. Given that we had three individuals, there were only three possible splitting combinations: (1) blocks from Calf2 and Calf3 as training set (5,766 blocks, 4. 5% suckling and 95. 5% non-suckling) and blocks from Calf1 as testing set (2,061 blocks, 2. 6% suckling and 97. 4% non-suckling), (2) blocks from Calf1 and Calf3 as training set (7,256 blocks, 3. 6% suckling and 96. 4% non-suckling) and blocks from Calf2 as testing set (571 blocks, 8.
DISCUSSION Our study characterized the suckling behavior in humpback whale calves less than 3-month old using CATS cam tags on one calving ground in the South Western Indian Ocean. Several important aspects of this vital behavior were investigated: (i) duration and frequency, (ii) occurrence in the water column, (iii) modalities, (iv) behavioral signatures, and (v) the possibility to use only acceleration and depth-derived data (i. e. , depth, depth rate, speed, Fluke Stroke Rate (FSR), Overall Dynamic Body Acceleration (ODBA), pitch, roll, roll rate, which were obtained using the data from the depth sensor and/or the 3-axis accelerometer only) to perform an automatic detection of suckling events.
Behavioral signatures of suckling events The comparison of the data derived from accelerometer and depth sensor for suckling and non-suckling events revealed mainly one distinctive characteristic of the suckling behavior: a high absolute roll. Other characteristics, although not obvious, such as high FSR and low speed are also suggested by the results of our comparisons. For suckling occurring during the descent phase, there were additional notable specificities: a low depth rate and a pitch close to zero. This is in sharp contrast with descent non-suckling where calves showed a high descent rate on average and were directed downwards. To our knowledge, no study to date has highlighted the characteristics of suckling behavior of whales in terms of roll recorded by an accelerometer. The sustained roll deviance from zero during suckling events is directly related to the aforementioned rolling pattern observed in the videos: rolling to the side may facilitates access to a particular mammary teat.
Automatic detection of suckling using accelerometer and depthderived data In the field of ethology, the advances in machine learning have offered the opportunity to classify behaviors within a complex database  (Valletta et al. , 2017). Given the stereotyped traits of suckling behavior in humpback whale calves, we tested whether it was possible to apply supervised machine learning algorithms on labelled accelerometer and depth sensor data to automatically discern suckling from non-suckling periods. Although accelerometer data have been already largely coupled with supervised machine learning to detect behaviors in various species  (Nathan et al. , 2012; Carroll et al. , 2014; Ladds et al. , 2016; Jeantet et al. , 2020) , our study is the first attempt to use it for humpback whales and in the framework of a suckling behavior study.
, 2011; Leevy et al. , 2018). Thus, class imbalance is less of a concern. In order to avoid evaluation bias, the evaluation process was guided primarily by the minimization of the FPR rather than the Global accuracy, since the latter does not distinguish between the numbers of correctly classified examples of different classes and can be misleading in the framework of an unbalanced dataset  (Galar et al. , 2011). It is important to recall that we also performed a data segmentation (we excluded blocks at <1. 5 m depth or at >2 m s -1 speed) that helped in reducing data imbalance and in improving the results. While the thresholds we chose for the segmentation works well with our data and are compatible with our current knowledge on the suckling behavior of humpback whale, they may be subject to change and adjustment as our knowledge on humpback whale behavior evolves.
Funding This work was supported by the  CNRS  and the Cétamada association. Maevatiana N. Ratsimbazafindranahaka  received a cotutelle PhD scholarship from the  ADI2020  project funded by the  IDEX Paris-Saclay ,  ANR-11-IDEX-0003-02. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
TEM Analysis Images were acquired with a Hitachi H 7650 electron microscope. Quantities of 6 µL of NP suspension were deposited on carbon film grid for 2 min 30 prior to drying at room temperature. Contrast was applied using Uranyless for 2 min before drying. For compositional mapping and energy-dispersive-X-ray spectroscopy (EDS), a ThermoFisher Talos F200S G2, operated at 200 kV, was used combined with a STEM (scanning/transmission electron microscopy) unit and a STEM-HAADF (high angle annular dark field) detector. X-EDS spectra were accumulated for 2 min and compositional mapping was performed for 15 min on uncontrasted samples. VELOX software was required for data acquisition and processing.
Acknowledgments: The authors thank all the students who participated to the work lab at the early stage or were partially associated with the project: Anthony Grelier, Marine Januel, Johannes Gisquet, Thais Tunes, Jeanne Gracianette and Jules Simonin-Garcia. The authors also acknowledge Brune Vialet and Patricia Korczak for the synthesis of oligonucleotides, Yannick Mousli for rapid mixing, Nathalie Peyron for bacterial culture and Lara Moumné and Nicolas Crouvezier for scientific discussions and their help with funding and IP issues.
A common reporting format has been agreed to (UNFCCC  [97] ) and will include the same level of details than current national inventories reported by so called Annex-1 countries (the countries that engaged on the Kyoto Protocol, that is all OECD countries, Russia and Kazakhstan). The Enhanced Transparency Framework represents a major challenge for developing countries that represent altogether 70% of anthropogenic emissions. These have provided hitherto sparse and simplified reports in the form of National Communications and/or Biennial Update Reports to the UNFCCC  [ 19, 94, 96]  and will be granted some flexibility by decision 18/CMA. 1 (UNF-CCC,  [95] ). Many developing countries do not have an infrastructure to systematically collect and analyze data on energy use, agriculture and the Land Use, Land Use Change and Forest sector (LULUCF).
Scaling up NGHGI approaches to the global scale in a way that is consistent with the global growth rates of atmospheric CO 2 , CH 4 (and N 2 O, not discussed here) is not straightforward because of the NGHGI focus on anthropogenic fluxes, different methodologies used by NGHGIs, missing reports from a few countries, sporadic and often outdated national reports in National Communications/ Biennial Update reports, uncertainties arising from different definitions of sectors and activities, and limitations in data collection. Global datasets and spatially explicit models such as those used in Global Carbon Budgets, GCB  [30, 31]  can make a valuable contribution to the GST by providing an independent, regularly updated and consistent means of linking global to national GHG budget. First attempts to link global approaches to national inventories have been recently made by  [9, 19, 86, 91]  for atmospheric inversions, and by  [37]  for global dynamic vegetation and bookkeeping models.
Engaging in an open dialogue between the scientific community and national inventory agencies that produce NGHGI is fundamental to reduce sources of uncertainty in the different estimates of GHG budgets, improve comparability of different approaches (definitions, sectors, uncertainties of each approach) and to identify mismatches that can reveal problematic sectors  [19, 38, 71]. A key challenge to this use of global budgets is that datasets from the global budgets from the Global Carbon Project  [31, 81, 92]  are themselves prone to large uncertainties in (i) the spatial distribution of surface GHG fluxes, at the scale of large regions, and even more over small regions or countries due to model structural differences, parameter values and model input data, (ii) the land-use, land-cover change and management (LULCC) input datasets used to estimate corresponding fluxes (F LUC ) by bookkeeping models (BK) and Dynamic Global Vegetation Models (DGVM) used by GCB, (iii) the attribution of fluxes to human vs.
The REgional Carbon Cycle Assessment and Processes project phase-2 (RECCAP2), part of the Global Carbon Project, aims to produce the best possible regional budgets of CO 2 , CH 4 and N 2 O in a globally consistent way while accounting for both emissions covered by GHG inventories and terrestrial and oceanic fluxes not covered by those inventories. The pilot project by the European Space Agency Climate Change Initiative (ESA-CCI) aimed at evaluating the potential of long-term global satellite Earth Observation archives to support REC-CAP2 and the GST by promoting close interactions and discussions between the scientific community and four National Agencies responsible for UNFCCC NGHGIs and other relevant institutions in five countries (Brazil, France, Germany, Italy, United Kingdom, UK). A focus of the ESA-CCI RECCAP2 pilot project was to improve the use of EO data in models used in GCB, through practical case studies including the use of satellite GHG concentration measurements for atmospheric-based models (inversions) of GHG fluxes, and the use of satellite observations allowing to improve estimates of biomass change and attribute them to different land use practices.
As a result of this dialogue and in light of recent studies, here we discuss the potential of selected EO datasets to contribute to the GST and outline a roadmap for implementation of an EO GHG-monitoring program to support the GST. We focus on the practical lessons we learned from using satellite EO data of GHG atmospheric concentration and land cover change to assess national scale GHG budgets. A summary of the models and datasets used here is provided in Table  1. This is not an exhaustive list, as many ECVs from EO data-streams exist that could be used to improve GHGIs (wetlands, fires, climate, permafrost, industrial or urban activities …). We nevertheless identify ECVs that we consider key priorities to improve estimates of natural vs. anthropogenic GHG fluxes for comparison and verification of national GHG budgets.
EO datasets for independent monitoring/verification of national GHG budgets in top-down CO 2 and CH 4 inversions approaches Top-down approaches allow estimating spatially-explicit and globally consistent land-and ocean-atmosphere fluxes, thereby providing a means to link country-level fluxes to global budgets. Inversion-based solutions are consistent with the global growth rate of GHGs which is not the case for bottom-up methodologies. However, current global inversions have coarse spatial scale, and country-level fluxes are still relatively poorly constrained, at least for medium-sized and smaller countries, especially in geographic regions with sparse atmospheric observation networks and unfavourable observation conditions from space (clouds, lack of insolation, etc. ). Comparisons with bottom-up estimates require adjustments to consider processes that are actually excluded/ included in each approach.
In addition, there is uncertainty in datasets required to make the adjustments needed to 'post-process' inversions to enable comparison with bottom-up methods and especially the separation of anthropogenic and natural fluxes for comparison with NGHGI  [19]. Successful examples of the use of inversions for CH 4 national emissions in large emitters countries for the agriculture and waste and the fossil fuel sectors are given in  [19] , and for oil/gas very large leaks of CH 4 , localised in extraction basins and along pipelines by  [60]  and ultraemission events  [53]. Namely, several oil and gas extraction countries like in the Persian Gulf region, Central Asia, and Russia, report significantly less fossil CH 4 emissions than what is constrained by GOSAT-based satellite inversions  [19]. In addition, very large CH 4 leaks (> 20 tCH 4 per hour) systematically detected and quantified using data from the TROPOMI satellite  [60]  are ignored by NGHGIs and can correspond to between 10 and 80% of national reported emissions.
g. comparable to those of deforestation in Brazil  [82]. We propose the following recommendations for the use of EO in top-down inversions estimates: 1. Systematic evaluation of the different satellite EO datasets on GHG column concentrations used as input of inversions, both through inter-comparison between datasets and their benchmarking against independent measurements made from the AirCore Atmospheric Sampling System  [55]. Remote sensing data from the ground by the Total Carbon Column Observing Network (TCCON,  [101, 105] ) or the Collaborative Carbon Column Observing Network (COCCON,  [29] ) could also be used for this purpose provided they further reduce their systematic errors. 2. Systematic evaluation of the performances of global inversions using independent cross validation (aircraft) and sanity checks (fit to growth rate) as performed for the inversions used by the global CO 2 budget, but not by the CH 4 budgets to date.
Stipulating and reporting clearly the methods used to make the adjustments to 'post-process' inversions' fluxes to enable a more accurate comparison with bottom-up methods, especially the attribution of emissions to specific inventory sectors/categories and separation of anthropogenic and natural fluxes for comparison with NGHGI, and the removal of CO 2 fluxes due to lateral transport processes from inversions results, based on independent datasets  [13, 19]. 4. Systematic reporting of consistently defined managed/unmanaged lands by countries so that gridded inversion estimates of GHG fluxes can be accurately sampled over the managed land areas covered by NGHGIs. We recommend that spatially explicit datasets on managed/unmanaged lands are provided by all the countries  [37]. 5. Clarification of where forest degradation processes causing CO 2 losses occur in each country.
Degradation is not explicitly reported in NGHGIs when it occurs on managed land, and not reported at all when it occurs in unmanaged land. Therefore, the degree to which degradation CO 2 losses are counted by countries is not clear, as current sample-based forest inventory data are insufficient to characterise C stock changes in forest land remaining forest land. 6. As pointed out in the introduction, countries can decide to treat natural disturbances emissions and recoveries in different ways, which makes a comparison with top-down CO 2 inversions fluxes challenging. We recommend that disturbance areas and emissions are reported in a spatially explicit way so that a better comparison of NGHGIs and inversions can be possible 7. Some of the recommendations 3 to 6 can, in principle, be addressed by the incorporation of more EO data into atmospheric inversions.
Land cover EO datasets for improved estimates of fluxes from land-use and land-cover change and management Fluxes from land-use and land-cover change and management (F LUC ) are one of the most uncertain components of the global carbon budgets  [30, 31]. Uncertainties in F LUC arise from multiple sources: definitions and methods  [33, 37, 70, 72] , fluxes and management considered  [2, 88] , model parameterization  [4] , and the LULCC area information used  [32, 34, 78]. Besides, the use different IPCC guidelines in NGHGIs results in inconsistencies of scopes and category classifications for reporting the F LUC estimates. An important aspect to consider are the differences between the definition of LULCC, used in bookkeeping models and DGVMs, and that of LULUCF used in NGHGIs. Comparisons between each of these three approaches requires adjusting the definition of these fluxes for the land domains considered (e.
g. managed vs. unmanaged, definition of forests, …) as well as in the processes included in each. Since these differences have been extensively discussed elsewhere  [33, 37, 38, 72] , here we refer to F LUC following the perspective of the Global Carbon Project carbon budgets, but note that most recommendations can in principle be applied to NGHGIs as well. Satellite remote-sensing is driving developments of spatially explicit land-cover and land-cover change datasets. Brazil is the single largest country emitter to global F LUC on average and therefore an excellent country to study this particular problem. Rosan et al. [78]  made a promising prototype assessment of how new remotesensing data can be combined to target LULCC dataset improvement in key countries. There, a high-quality long-term LULCC dataset based on Landsat data has been developed by "The Brazilian Climate Observatory" (Mapbiomas), providing a unique reference data to evaluate global LULCC datasets.
Rosan et al. [78]  compared F LUC estimates based on Mapbiomas with F LUC estimates based on two datasets used in Global Carbon Budgets  [30, 31]. The HYDE3. 2 global dataset  [57] , used in the Land-use Harmonization (LUH2) to drive DGVM and one bookkeeping model in past global CO 2 budget assessments  [10, 30]  relied on a fixed 300 m land-cover reference map by ESA Land-Cover CCI (LC-CCI,  [18] ) to spatially allocate LULCC. In the GCB2021 updated version of HYDE (3. 3), annual LC-CCI maps were used to spatially constrain LULCC, resulting in better agreement in the spatial patterns and trends of F LUC in Brazil with estimates based on Mapbiomas. The recently published HILDA +  [103]  dataset reconstructed LULCC since 1960 at intermediate spatial resolution (1 km) using multiple remote-sensing based datasets, rather than a single one.