dataesr/scientific-paragraphs-categorization huggingface dataset:
Dataset description and behavioural labelling The used dataset contains the tri-axial acceleration and angular velocity of 13 free-ranging green turtles recorded at a frequency of either 20 Hz or 50 Hz  (Jeantet et al. , 2020b (Jeantet et al. , , 2020a)). The depth profiles are also provided at 1 Hz. The devices were combined with a video recorder allowing us to validate the signals. The video footage, analysed using a handmade software written in python language  (Hadetskyi, 2019)   Geiger, 2019). To test the V-net, we synchronize the observed behavioural categories with the acceleration, angular velocity and depth data, subsample the 50 Hz data at 20Hz and remove the unlabelled section. We focus in particular on the Feeding category, which is particular difficult to discriminate. In this study, the green turtles mainly fed seagrass on the ground from which we identified two distinct behaviours: the action of pulling on the seagrass that we referred as Grabbing, and the action of mastication referred as Chewing.
V-net architecture The V-net was first described and developed by  Milletari et al. (2016)  to treat medical images (3D signals). It is an evolution of the U-net, developed for biomedical 2D images  (Ronneberger et al. , 2015). The implementation of the both algorithms can be found in the respective articles  (Milletari et al. , 2016; Ronneberger et al. , 2015). In this work, we present an adapted architecture for the V-net which fits with our multi-sensor data, i. e. 1D temporal series (Figure  2 ). It has been implemented with the Keras and Tensorflow libraries through their python interface. Depth corresponds to the number of features to create (depth = 32), nb_desc to the number of input data and nb_classes to the number of behaviours (nb_classes=7). We choose a window size of 40 sec (WS= Window_size=800).
We also compare the predictions of the model to the true behaviours through an overall confusion matrix. Thus, the well-identified behaviours (TP: true positive, TN: true negative) and the misclassified windows (FN: false negative, FP: false positive) are identified and used to calculate the classical indicators for each behaviour  (Powers, 2011) : &' ( = ) ) + +, ) ' -. -/0 = ) ) + +) In order to compare the ability of the network to identify the behaviours of sea turtles to the previous study on the same dataset, we calculate the global accuracy  (Powers, 2011)  : 1 /2( 3 ( 4 = ) + , ) + , + +) + +,.
Variable importance We attempt for each case (case I, II, III and IV) to identify the most important input variables in the classification process, i. e. those containing the most information and mostly used by the V-net. We adapt the class activation map (CAM) visualization  (Chollet, 2018; Selvaraju et al. , 2019)  developed in image classification from deep learning. From the tested dataset, we compute the gradients of the model's prediction for one given behaviour to the input features and average their absolute value. We obtain thus the contribution of each single input variable on the classification of one specific behaviour. Moreover, we also perform a variable selection by backward elimination to identify the impact of each variable on the classification accuracy  (Derksen and Keselman, 1992; Heinze et al.
Sensitivity and Uncertainty analyses Deep learning are known to be very sensitive to small perturbation  (Shu and Zhu, 2019). We test the sensitivity of the V-net by classifying sea turtle behaviours from degraded data. We randomly select 500 windows (window size = 40 sec) from the tested dataset and gradually add some noise on each input variable for each time t_i: 5 6 _ = 5 _ + 8 * , _ Where Nt_i are standard Gaussian random variables and 8 is a constant that we increase from 0 to 0. 5. We perform the AUC score on the obtained classification and visualisation its evolution according to the noise level (Figure  9 ). In this way, by inducing a σ value of 0. 05, corresponding to add 5% of noise to the inputs as they are also standardized, we impact the AUC by 5% (AUC score = 80. 49 % against 85.
67 without noise). However above 10% of noise, the performance of the V-net drops. We measure the uncertainty of the model using Monte Carlo Dropout method  (Abdar et al. , 2020; Gal and Ghahramani, 2016). This method consists in predicting a single window multiple times (n =1000) but with dropout layers activated in the prediction process, forcing the V-net to mask some connections. The variance of the predicted probabilities indicates the uncertainty of the model. Figure  10  shows the obtained variance for two windows from the tested dataset in Case I, where the V-net misclassifies Feeding as Scratching and, at the opposite, successfully identifies it. It appears that, most of the time, the correct predictions are associated with low uncertainty while the variance increases for the misclassified parts.
Advances in automatic behavioural classification using deep learning The V-net improves the automatic animal behavioural classification from multisensor datasets by avoiding the segmentation problem. Classical machine learning requires cutting the dataset into segments from which statistics (maximum, mean, minimum, standard deviation, etc. ) are calculated and used to feed the model. It is a particularly difficult processing step as it has to be automatic, in the real application conditions (i. e. without knowing the beginning and ending of the behavioural labels) and impacts the performance of the algorithms. To date, the most common segmentation method has been the windowing approach based on fixed time segments  (Lagarde et al. , 2008; Martiskainen et al. , 2009; Watanabe et al. , 2005)  or a sliding window with a fixed length  (Ladds et al.
Although Scratching has been rarely reported in sea turtle behavioural research using animal-borne video recorder  (Arthur et al. , 2007; Okuyama et al. , 2013; Reina et al. , 2005; Seminoff et al. , 2006) , it seems to have an important physiology role as it could be associated with a cleaning function  (Heithaus et al. , 2002; Thomson and Heithaus, 2014). However, in general, behaviours with a small number of observations available and associated with fine-scale signals, such as Feeding and Scratching, are hard to detect by an automatic method  (Jeantet et al. , 2018; Wang et al. , 2015). The V-net has been thought to deal with strong imbalance dataset and/or limited in the number of labelled data  (Milletari et al. , 2016). Hence, enhanced by an adapted Loss function and a biased random drawn for the training process, the convolutional neural network precisely detects both behaviours and slightly overestimates Scratching.
Conclusion Deep learning is an emerging field that is particularly powerful for automatically processing large datasets and the information they contain. It is therefore not surprising to see its application extended to ecology by the automatic behavioural identification from multi-sensor recorders. An adapted fully convolutional neural network, the V-net, reveals high ability to automatically identify the behaviours of sea turtles from multisensor signals and outperforms classical machine learning. Its use is particularly interesting to avoid the segmentation process and the manual calculation of a large number of discriminant features required, until now, to identify the behaviours from accelerometer. We also show in this study that that the gyroscope is more informative that the accelerometer in identifying the behaviours of sea turtles and that the V-net is not able to discriminate fine-scale behaviours from the raw data of accelerometer alone.
Training and tuning processes are performed simultaneously through 150 epochs. An epoch corresponds to one pass through the training dataset where 15,000 windows are drawn to feed the optimizer by batch of 200 (batch_size = 200). At the end of each epoch, we observe the loss relative to 3,000 windows of the validation dataset (separate from the training dataset). Over the 150 epochs, we finally keep the weights corresponding to the lowest validation loss. The hyper parameters are tuned in order to best reduce this validation loss. The main ones are the depth which corresponds to the number of created features (depth = 32), the learning rate lr regulating the speed of the gradient descent (lr = 0. 01), the batch_size and the sampling probabilities of behaviours.
Invasive alien shes have caused pernicious ecological impacts on aquatic ecosystems. However, there has not been a global appraisal of associated economic impacts. Here, we compiled reported economic impacts of invasive alien shes using the most comprehensive global database of invasion costs (InvaCost). We analyze how sh invasion costs are distributed geographically and temporally, as well as which socioeconomic sectors are most impacted. Fish invasions have caused the economic loss of at least US$32. 8 billion globally (2017 value), from only 26 reported species (of 128 known invasive alien sh species). North America had the highest costs (> 99%), followed by Europe and Asia, with no costs reported in Africa, Oceania nor South America. Very few costs from invasive sh in the marine realm were reported (0.
, 2013 , Schlaepfer et al. , 2011 , Katsanevakis et al. , 2014 , Gozlan 2015 , 2016) , or other perceived ecosystem bene ts  (Pejchar & Mooney, 2009). To address this pervasive knowledge gap and provide a baseline for cost quanti cations, we characterise, for the rst time, the current status of knowledge on global costs of alien invasive shes using the InvaCost database  (Diagne et al. , 2020). This database contains detailed information on reported costs (e. g. cost types, impacted sectors, regional attributes, reliability of cost estimations, etc. ) over the past 60 years, associated with ~ 800 invasive species from all ecosystem types globally. In the present study, we use a subset of this database to which we add complementary cost information from other sources. Our aims were to describe the global costs associated with alien invasive sh species, explore the structure of these costs, and to identify potential knowledge gaps and biases in the estimation of past and current economic impacts.
Cost data sourcing and ltering To estimate the cost of alien sh invasions reported globally, we considered cost data from the InvaCost database  (Diagne et al. , 2020). This database compiles 2,419 cost entries in a su ciently detailed manner for large-scale syntheses of costs associated with invasive species at different spatial and temporal scales. We complemented the data of the InvaCost database in two ways. These new references were thoroughly assessed to identify relevance and extract cost information. Ultimately, all costs were converted to 2017 US$ values (see  Diagne et al. 2020  for detailed information). The enhanced database used for this analysis includes information on monetary costs across taxonomic, regional and sectoral descriptors, and allows the distinction between observed (i.
e. costs of a realized impact) and potential costs (i. e. costs of a predicted/expected impact), as well as the reliability of methodologies used for cost estimates (high or low reliability). We ltered the InvaCost database in order to only keep costs related to shes belonging to the classes Cephalaspidomorphi and Actinopterygii. Database entries not attributable to unique species, sectors, or cost types within those classes were categorized as "Diverse/Unspeci ed". All analyses were performed for the period between 1960 to 2020, given that (a) monetary exchange rates prior to 1960 were unavailable, and (b) 2020 was the last year for which cost data were considered in the database. The nal dataset used for the analysis is provided in Supplementary Material 1.
Finally, the cumulative invasion costs were estimated based on their classi cation under the following cost descriptors (i. e. , columns) included in the database (Supplementary Material 2): (i) Method_reliability: illustrating the perceived reliability of cost estimates based on the type of publication and method of estimation. Costs are considered as of low reliability when materials cannot be assessed for full-text investigation or if costs come from grey literature with no fully described method. On the contrary, costs are considered as of high reliability they come from peer-reviewed articles, o cial documents, or grey literature with fully described method  (Diagne et al. , 2020) ; (ii)Implementation: referring to whether the cost estimate was actually realised in the invaded habitat (observed) or whether it was extrapolated (potential).
Temporal cost accumulations To assess temporal trends of invasive sh species, we considered 10-year means since 1980, because cost data concerning sh invasions were reported solely after the 1980s. We examined costs as a function of the year of impact, which re ects the time at which the invasion cost likely occurred and expanding it over years during which the costs was realised (using the probable_starting_year and probable_ending_year columns; see  Leroy et al. , in prep. ). This allowed for an estimation of annual average costs over the entire reported period, as well as over decadal increments.
Comparison with other taxonomic groups In order to put costs of alien invasive sh species in a wider taxonomic perspective, we compared the economic costs of shes with those of invasive birds and mammals. The comparison was based on the total cost and the number of cost entries in the InvaCost database, coupled with the number of invasive species per taxon, as well as the numbers of scienti c publications in invasion science. First, total monetary costs and database entry numbers for birds and mammals were calculated following the methods detailed above. Second, we estimated the available literature for each group using the same search protocol as the one used for the InvaCost database (see  Diagne et al. 2020) , excluding words that refer to costs and adding the biotic group name (i. e ' sh", "mammal", or "bird"), in order to get a comparative proxy of research effort in invasion biology for these three taxa.
Exact search strings used can be found in Supplementary Material 3. The information considered in this comparison was gathered using the Web of Science. Third, the numbers of alien species for each of the three aforementioned taxonomic groups were estimated using the IUCN Red List database (https://www. iucnredlist. org/). We categorized alien species according to their IUCN status, either "Extant and Introduced", "Possibly extinct and Introduced", "Presence Uncertain and Introduced" or "Possibly extant and Introduced". Last, we used Pearson's Chi-squared test to assess whether the data of three taxonomic groups had the same distribution of variable values (number of alien species, number of cost entries, number of studies reporting invasion costs and total costs).
Results A total of 228 cost entries for 26 alien invasive species from 17 sh families were identi ed in the database, summing up to US$32. 80 billion. The majority of costs, however, was deemed as potential (US$31. 27 billion; n = 164, hereafter the number of cost entries), while observed costs summed up to just US$1. 53 billion (n = 64). In turn, the majority of costs (US$20. 29 billion; n = 182) were deemed as highly reliable, with US$12. 51 billion (n = 46) evaluated as of low reliability (Supplementary Material 4).
Comparisons across biotic groups Records for alien shes from the IUCN Red List database (n = 128), were approximately half the number of recorded alien birds (n = 207), but were very close to the number of recorded alien mammals (n = 108). Conversely, shes comprised the taxonomic group with the largest number of scienti c publications on alien species (15,969 papers), which was approximately double the number of publications for birds (7,900) and four times more than mammals (4,334) (Figure  5 ). Invasive sh species, however, had the lowest number of entries before expansion over time (33) compared to mammals (292) and birds (43). In turn, the total cost of invasive sh species (US$32. 80 billion) was found to be much lower than mammals (US$261. 24 billion), but greater than birds (US$6. 55 billion).
Cultural biases In Europe, the topmouth gudgeon (P. parva) was identi ed as the costliest sh invasion based on solely six studies reporting reliable and observed costs, and despite the availability of further cost information which have not been incorporated into the InvaCost database. That is, its introduction in UK waters in 1985, has led to the largest and costliest eradication programme ever taken on for a sh species  (Britton et al. , 2010). With an average annual cost of about £190 thousand (~ US$244 thousand) over a three-year period, about thirty populations distributed across England and Wales have now been eradicated  (Britton et al. , 2011). Another economic impact assessment that considered potential future distributions for P. parva indicated potential resource damages and losses totalling between £2.
Funding The authors acknowledge the  French National Research Agency  ( ANR-14-CE02-0021 ) and the  BNP-Paribas Foundation Climate Initiative  for funding the  Invacost  project that allowed the construction of the InvaCost database. The present work was conducted following a workshop funded by the  AXA Research Fund Chair of Invasion Biology  and is part of the  AlienScenario  project funded by  BiodivERsA and Belmont-Forum  call 2018 on biodiversity scenarios. RNC is funded by a  Research Fellowship  from the  Alexander von Humboldt Foundation. CD is funded by the  BiodivERsA-Belmont Forum  Project " Alien Scenarios " ( BMBF/PT DLR 01LC1807C ).
costs Observed costs Common name Species Environment Costs (US$2017 in millions) Database entries Costs (US$2017 in millions) Database entries F Goldfish Carassius auratus 0. 001 3 0. 001 3 F,B Ruffe Gymnocephalus cernua 28,933. 600 48 0. 383 1 F,B Sea lamprey Petromyzon marinus 2,413. 636 12 61. 359 4 M, F , B Black bass Micropterus salmoides 5. 293 34 5. 293 34 F Silver-cheeked toadfish Lagocephalus sceleratus 4. 186 14 3. 873 6 M White bass Morone chrysops 3. 394 1 F Topmouth gudgeon Pseudorasbora parva 2. 193 11 0. 575 6 F,B Common carp Cyprinus carpio 1. 859 16 1. 859 16 F,B Brown trout Salmo trutta 1. 782 10 M,F,B Common minnow Phoxinus phoxinus 1. 21 3 1. 210 3 F,B Chinese sleeper Perccottus glenii 0. 173 4 F,B Northern snakehead Channa argus 0. 138 1 0. 138 1 F Bluegill Lepomis macrochirus 0.
The work presented herein builds upon and improves the relevant recent work on this subject. It adds countrylevel details and technical sub-sector descriptions to the global synthesis of Rosenzweig et al  (2020). It extends the recent country-level database and analysis of Crippa et al  (2021) , which extended only to the year 2015, additionally incorporating more recent data on food-relevant land use change components, specifically in relation to forests  (Tubiello et al 2021)  and peatland fires (FAO 2021a). We complete our accounting with regional and global estimates of emissions from energy use in food supply chains. Finally, we discuss how the new set of food system categories can help inform food-related mitigation strategies.
Food system components beyond agricultural land Relevant data on GHG emissions generated on agricultural land were available in FAOSTAT with country detail over the period 1990-2018. In addition in this work, we developed new food system data, at the country level for the period 1990-2018, for: (a) 'domestic food transport,' defined as the subcomponent of the IPCC Energy sector covering mostly CO 2 emissions from commercial and household food transport; and (b) 'food waste disposal,' defined as the sub-sector of the IPCC Waste sector covering CH 4 emissions from the decomposition of solid food waste in landfills, CO 2 emissions from the incineration of food waste materials, and CH 4 and N 2 O emissions from domestic and industrial wastewater management. Finally, we developed regional and global estimates of food system emissions from energy use in food supply chains, based on published work by  FAO (2011).
Food waste disposal We first estimated the mass of solid food waste disposed to landfills by country, by applying food waste data from the IPCC guidelines (  2019 ) to solid waste destination data from the World Bank (Kaza et al 2018). A log-linear relationship between per capita solid waste disposal and per capita GDP was used to estimate historic solid food waste deposition in landfills  (Thi et al 2015 , Xue et al 2017 , Singh et al 2018). The latter was used as input into a climate-sensitive first order decay model to estimate CH 4 emissions from landfills in a given inventory year. We also included estimates of food systems emissions from domestic wastewater, industrial wastewater and incineration by country and over the same period 1990-2018, while recognizing that these components are relatively small compared to emissions from solid food waste.
Domestic food transport Emissions from food transport consist of CO 2 , CH 4 and N 2 O from fuel combustion for domestic civil aviation, road transportation, railways, domestic water-borne navigation, and other transportation. We estimated these emissions by country and over the period 1990-2018, as a fraction of transport emissions reported by a version of the PRIMAP dataset with increased sectoral resolution (Gütschow et al 2021). Food transport activity data and emissions factors for the United States, China and the European Union-representing roughly 50% of world-total transport emissions, according to PRIMAP-were estimated with coefficients taken from published reports (USDA-ERS, EU-JRC 2015, Canning et al  2017 , Song et al 2019)). For all other countries we took the relevant coefficients from Crippa et al  (2021).
Secondly, we did not consider possible carbon sinks on agricultural land, for instance those generated by tillage practice, despite the recognized role of reduced tillage or no-till as a carbon sequestration technique  (Debska et al 2020 , Feng et al 2020). Insufficient data coverage at national and regional scale limit such estimations currently. At the same time, we assumed that eventual removals over the study period were likely to be small compared to the land use emissions that were instead considered, i. e. those from peat drainage. Third, the data used for on-farm energy use also included energy use in forestry and fisheries, although data for these latter categories are quite incomplete. This implies that we may be overestimating emissions from on-farm energy use, or at least that we should use them to cover the full range of emissions from the agriculture, forestry and fishing sector, once more complete data coverage is available from the original United Nations Statistics Division (UNSD) source.
Land use change emissions New forest emissions and removals data for the period 1990-2020 (Tubiello et al 2021) were used for this assessment. They show a 30% decrease in net forest conversion from 1990 to 2018 (from 4382 to 3034 Mt CO 2 eq yr -1 ), with emissions occurring almost entirely in NAI countries (table  1 ). Emissions from tropical peat fires were 204 Mt CO 2 eq yr -1 in 2018, 30% less than in 1990. These results were only one-fourth of previous FAO estimates (Prosperi et al 2020), used in recent assessments of food system emissions (Crippa et al 2021). In contrast, land use change emissions on agricultural land totaled 3238 Mt CO 2 eq yr -1 in 2018. Net forest conversion, used herein as a proxy for food-related deforestation, was the single largest emission source estimated on agricultural land over the entire 1990-2018 period (table 1).