dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2017; Zhang et al. , 2022). Such efficiency gains are generally associated with fewer emissions per unit of animal product produced  (Connor, 2015; Herrero et al. , 2016). Against this background, the aim of our study is to quantify GHG emissions caused by agricultural activities in Austria in 1830-2018 and to assess the roles of major drivers across time and agricultural product categories. In addition, we calculate annual and cumulative emissions based on the alternative global warming potential metric GWP*. Based on these results, we discuss data limitations, contextualize our results by emissions from historical industrialization dynamics, including fossil energy emissions and ecosystem carbon sinks derived from existing studies. We conclude by drawing conclusions for current climate change mitigation challenges.
Methods We employ methodologies outlined in  IPCC (2006 IPCC ( , 2019a)) ,  Hutchings et al. (2013) ,  FAO (2017)  and  Gingrich et al. (2021) , using datasets on short-term  (BMLFUW, various years; Statistik Austria, 2015 , 2018 ; UBA, various years) and long-term trends in land use and agricultural production  (Gingrich et al. , 2016; Gingrich and Krausmann, 2018; Krausmann, 2001a; Krausmann and Haberl, 2007) , to generate a time series of agricultural GHG emissions on the current territory of Austria in the period 1830-2018 with annual resolution. Within this time series, we identify major drivers of change by a decomposition analysis. Our results cover all agricultural CH 4, N 2 O and CO 2 emissions, including CO emissions during the use of tractors. In our calculation, we include and differentiate three categories of ruminant livestock (non-dairy cattle, dairy cattle, sheep and goats), three categories of monogastric livestock (pigs, poultry, horses), three types of livestock feed (food crops, fodder crops and grass, residues and litter) and 33 types of crops and grassland.
For the decomposition analysis, we express all emissions in tons of CO equivalents per year (t CO 2 eq/yr), using Global Warming Potentials (GWPs) with a 100-year time horizon according to the IPCC AR6 (N2O 273, CH4 27, IPCC, 2019b). The alternative time series of annual and cumulative GHG emissions, using GWP*, is based on the formula described in Section 2. 4. The dataset on historical land use, agricultural production and population relies on previous data compilations from domestic and international statistical sources  (Gingrich et al. , 2016; Gingrich and Krausmann, 2018; Krausmann, 2001a; Krausmann and Haberl, 2007) , which we updated to the year 2018 using data from FAO (2021b) and national statistics  (BMLFUW, various years; Statistik Austria, 2015 , 2018; UBA, various years). The dataset enables us to quantify emissions at the detailed level required to decompose aggregate trends into different drivers.
GHG emissions: calculation procedures and data sources Fig. 1  displays processes and associated GHG fluxes considered in our analysis. The description below gives an overview on calculation methods and data sources for the calculation of the GHG emission time series. A more detailed description of the emission calculation and an overview of the applied coefficients is provided in the supplementary material. CH 4 emissions from enteric fermentation (1) are calculated as a function of feed energy intake and a methane conversion rate, defined as the share of feed energy converted into methane during enteric fermentation. This corresponds to the tier 2 method according to IPCC (2019a). The methane conversion rate is calculated as a function of feed digestibility according to values from  INRA et al.
(2020)  and livestock category specific coefficients, applying equations from FAO (2017). Feed intake is calculated from feed supply according to  Krausmann (2001b) ,  BMLRT (2020 ), Statistik Austria (2015 , 2018)  and FAO (2021b), which is allocated to livestock categories according to basic principles, e. g. roughage is primarily allocated to ruminant livestock (see Section 1. 1 in the supplementary material for details). For roughage and fodder crops, we applied loss factors for harvest and storage according to BMLRT (2020), which we hold constant over time, as data on historical changes was not available. N 2 O and CH 4 emissions from manure management (2) are calculated according to the tier 2 method from IPCC (2019a). CH 4 emissions from manure management are calculated for each livestock category from volatile solids excreted, shares of manure management systems and pasture management, multiplied by manure and pasture management specific CH 4 emission factors.
N 2 O emissions from manure management are calculated for each livestock category as a function of excreted N, manure management system shares, the fraction of N volatilizing or leaching and N 2 O emission factors for direct and indirect emissions. N 2 O emissions from soils (3) include all N 2 O emissions caused by the application/dropping of livestock manure and the application of synthetic fertilizers (3). They are also calculated using a tier 2 method (IPCC2019a). N 2 O emissions from organic N application to soils and manure dropped on pastures are calculated as a function of N inputs to soils in the form of manure, residues and synthetic fertilizers, fractions of N volatilizing or leaching and N 2 O emission factors for direct and indirect emissions. N inputs from above-and belowground residues returned to soils are calculated from data on biomass production, the ratio between above-and belowground biomass and N content of belowand aboveground residues.
The excretion of volatile solids and N by livestock category is calculated from the livestock metabolism model described in  Gingrich et al. (2021). For the share of manure management systems and pasture management, we use data from Austria's National Inventory Report for the period after 1990. Before this year, due to a lack of empirical data, we developed plausible assumptions for 1830 for all livestock categories and interpolated values linearly between these years (see Section 1. 1 of the supplementary material for details). We also included GHGs emitted during the use of agricultural machinery (tractors). They are derived from annual stocks of tractors according to  Sieferle et al. (2006) , multiplied by factors for average annual fuel use per power class and tractor hours according to  Nemecek and Kägi (2007) ,  Lips (2017)  and ÖKL (2022).
Because livestock numbers were assessed with different methods and in different seasons throughout the 19th century, fluctuations in livestock emissions and total agricultural emissions during this period are not to be overinterpreted, while the general level and trends appear rather robust. Regarding (ii), studies using similar data and procedures point to an uncertainty of 25 %-35 % for the period since the 1960s  (Crippa et al. , 2021; Tubiello et al. , 2013). For earlier periods, we assume an even higher uncertainty due to the data uncertainties described above, as well as rough estimates performed to quantify manure management system shares. The underlying assumptions applied in our study (2) constrain the scope of our findings in two major ways: (i) By adopting a territorial approach on agricultural emissions in Austria, we exclude all emissions abroad which are induced by Austrian consumption.
Kapsenberg et al. (2017)  showed, thanks to a discrete (weekly) sampling strategy, that the rate of pH change was -0. 0028 ± 0. 0003 units pH yr -1 in a coastal Mediterranean site (Bay of Villefranche-sur-Mer) between 2007 and 2015. Gac et al. (2020)  also provided a robust multi-annual dataset from 2015 to 2019 based on hourly measurements deployed on a buoy in the southern Western English Channel. They demonstrated that the tidal transport of coastal and offshore water masses strongly impacted the daily and seasonal variability of the partial pressure of CO2 (pCO2), although biological productivity was the main driver of pCO2 dynamics with large inter-annual variability. Generally, rapid fluctuations of pH in coastal areas due to processes mentioned above frequently largely surpass the long-term pH trend.
, 2014). Each SeaFET of the network is deployed in an autonomous mode next to a CTD probe which is part of the following list: SMATCH MP3 or MP6 (NKE©), WiMo (NKE©), STPS (NKE©), WiSens CTD (NKE©), SBE37-SM (Sea-Bird Scientific©) and SBE37-SMP-ODO (Sea-Bird Electronics©). The acquisition frequency at each site depends on the local environmental conditions but is generally 15 min, except for COAST-HF MAREL Iroise (20 min) and COAST-HF ARCACHON Bouée 13 (10 min). A summary of the sensors used at each site, their deployment depth, acquisition frequency, the starting date of pHT measurements and the DOI associated with the publicly available datasets is provided in Table  1. Pictures of different setups are given in Fig. 2. On most sites, instruments are deployed on a sub-surface mooring mode thanks to pre-existing navigation channels or oceanographic buoys.
For three of them (Figuier, COAST-HF SMART Daoulas, ECOSCOPA Men-Er-Roué), SeaFET and CTD probes are deployed on benthic metallic structure with sensors at 50 cm above seabed (Table  1 ). It could reach up to five days for the internal electrode and more than 15 days for the external one. To avoid gaps in datasets, a pre-conditioning of the sensors is done with filtered seawater taken in the field site prior to the deployment. All the technical field maintenance (sensors cleaning, data upload, SeaFETs swap) is recorded in an Excel monitoring sheet for each site. The general recommendations that framed this network are detailed in  Bresnahan et al. (2014).
The analysis 280 results for each bottle are eventually kept depending on the SNAPO-CO2 quality flag (2: correct / 3: doubtful)  This last step mainly concerns salinity data. For ECOSCOPA sites and both sites located in the Bay of Morlaix, a similar approach is realized where the data acquisition relies on alternating deployment of instruments for short periods between 15 and 30 d. CTD probes are regularly calibrated either at the Ifremer Metrology Laboratory or directly at the manufacturer throughout annual maintenance. The Bouin facility processes their data and only transmits validated data. Total concentrations of silicate, phosphorus and ammonium are estimated upon samples taken at the nearest sampling point from either REPHY or SOMLIT networks. First, monthly climatological means are estimated for each site based on the overall dataset and a daily climatology is created.
Then, the combination of corresponding raw voltage signals and spectrophotometric pHT data is used over a specific deployment period. This period should correspond to the timespan when the SeaFET is deployed. However, it could be shortened by the operator and depends on the in situ calibration primarily controlled by the biofouling development. If an offset appears between the SeaFET and the discrete sample, an in-depth cleaning of both sensors is required as specified by the manufacturer. This physical, sometimes chemical, operation may modify the reference potentials and therefore induce a new deployment number. In case of no available discrete sample, the SeaBird factory coefficients are used to estimate pHT values. This last case is rare in our dataset and happened only on a few occasions, especially at the early stage of the network.
3). The terrestrial Bouin site also presents among the highest values of the dataset with large seasonal variations both for AT and CT (2367 ± 489 and 2182 ± 472 µmol kg -1 , respectively). The calcium concentration observed in the soil composition in Vendée  (Hájek et al. , 2021 ) is a plausible explanation for these data patterns. At the exception of the Bay of Arcachon, farming and oceanic sites do not present strong differences in their overall mean pHT level but farming sites generally present a larger seasonal amplitude (Table  3 ). The largest differences in seasonal amplitude between farming and oceanic sites are observed in the Mor Braz, the Bay of Arcachon and the offshore Sète in the Mediterranean area. It was already mentioned that the oceanic site in the Mor Braz is much more influenced by the Vilaine and Loire rivers than the farming site, resulting in overall lower salinity and pHT levels as well as stronger seasonal changes in these variables due to freshwater runoff.
Data availability In order to facilitate file organization, each site is assigned an acronym in the list given in Table  5. External high-frequency temperature and salinity data are given in a csv file called CTD_{site}. csv containing raw data with an appropriate quality flag to filter validated data (flag 1: good data). Another csv file called pH_{site}. csv contains validated pHT data together with temperature and salinity (interpolated when necessary). The history deployment information is given in another file named deployment_{site}. csv including detailed maintenance operations and noticeable remarks concerning the SeaFET electrodes. Eventually, the discrete sample measurements are gathered in samples. csv with result from PyCO2SYS at in situ temperature and nutrient data from the SOMLIT and REPHY databases.
Conclusion and present status of the network The network initiated in 2021 along the French coast area has provided essential data for the assessment of carbonate chemistry dynamics at various temporal scales and in contrasted coastal sites (shellfish farms located close to the shore vs sites with no shellfish farming more subjected to oceanic conditions). The large number of sites and geographical coverage of the network has already allowed us to evaluate the influence of diverse physical, chemical and biological processes (freshwater inputs, tides, temperature, biological processes) which we briefly presented in the present manuscript. The acquired dataset will undoubtedly be of great interest to the public and scientific communities in the future as our choice to base our network on existing monitoring activities not only allowed providing reliable data at a very high acquisition rate and at a lower financial cost, but also the possibility to rely on existing low frequency datasets (chlorophyll, nutrients, organic matter concentrations etc.
3 Data processing 2. 3. 1 Temperature, salinity and nutrient databases For COAST-HF stations, high-frequency temperature and salinity are recovered in different ways as this network has not a unified data qualification process. The first step consists in gathering files of different sources, official CORIOLIS database (https://www. coriolis. eu. org) and local files from additional spare probes, into a single one for each station. Then, based on low-frequency measurements available within REPHY and SOMLIT networks, doubtful data are identified by flagging them. 285 Region Name Position Instruments Depth pHT monitoring Nutrients Official DOI Bay of Figuier 48°40'28. 1'' N 3°56'09. 3'' W WiSens CTDS SeaFET pH 50 cm above seabed 15 m below Chart Datum Mar 2021 15 min REPHY Pont de la Corde Morlaix Bloscon 48°43'43.
1. In this paper, we investigate whether deep learning models for species classification in camera trap images are well calibrated, i. e. whether predicted confidence scores can be reliably interpreted as probabilities that the predictions are true. Additionally, as camera traps are often configured to take multiple photos of the same event, we also explore the calibration of predictions at the sequence level. 2. Here, we (i) train deep learning models on a large and diverse European camera trap dataset, using five established architectures; (ii) compare their calibration and classification performances on three independent test sets; (iii) measure the performances at sequence level using four approaches to aggregate individuals predictions; (iv) study the effect and the practicality of a post-hoc calibration method, for both image and sequence levels.
The DeepFaune Dataset We use the dataset of the DeepFaune initiative  (Rigoudy et al. 2023) , which is a collaborative effort involving over 50 partners who, together, have gathered over two millions images and twenty thousand videos that they had manually annotated. These partners are affiliated to a wide range of institutions, such as organizations managing protected areas, hunting federations, and academic research groups. Images and videos were mainly collected in France, but also in a few European countries. Most of the annotation were at the species level, but some were at a higher taxonomic level (e. g. mustelid). Videos were converted into images by extracting frames of the first four seconds, with a time step of one second. The dataset provides a great diversity of habitats, elevations and weather conditions, as well as a wide variety of camera trap models with different settings, resolutions, flash type and image processing.
Training and validation datasets For the species classification task, it is now known  (Beery, Morris, et al. 2019; Norman et al. 2023 ) that two-step approaches (object detector followed by a classifier) are more efficient than classifiers that process the whole image. We use MegaDetector v5 (MdV5)  (Beery, Morris, et al. 2019)  to extract bounding boxes of animals, human and vehicles. Because MdV5 has already near-perfect accuracy on human and vehicles we only kept, for the training of our classifier, the bounding boxes that predicted the presence of an animal. For each bounding box, we created a cropped image of the original image, resulting in 429 347 cropped images of 22 different classes (the distribution of the classes is shown in Supporting Information Figure  1 ). To avoid overfitting and shortcut learning between the background of the images (i.
Out-of-sample test sets To demonstrate that the results of the classifier could generalize beyond the images collected in the DeepFaune initiative, 3 out-of-sample test datasets were used. These datasets originated from ecological programs conducted in three geographically distinct areas. We refer to these datasets by the name of the areas they originate from: • Pyrenees : camera trap study in the national reserve of Orlu in the French Pyrenees, conducted by the French Biodiversity Agency (OFB), 100 266 images and 12 species after preprocessing. • Alps : camera trap study in the Ecrins national park in the French Alps, conducted by S. Chamaillé-Jammes, 8 106 images and 12 species after preprocessing. • Portugal : camera trap study in the Peneda-Gerês National Park in Portugal  (Zuleger et al.
Temperature Scaling Temperature scaling  (Platt 2000 ) is a post-processing method to improve the calibration of the model after the training. The scores predicted by the model are rescaled by a temperature parameter T > 0 using a generalization of the softmax function : p ij = exp z ij /T K k=1 exp z ik /T (9) For T = 1 the scores obtained are the same as with the standard softmax function. T > 1 leads to lower scores and helps when the model is over-confident. Conversely, T < 1 increases the scores and helps under-confident models. For a given dataset, it is possible to determine the optimal temperature T * , that minimize the ECE. However, this optimum temperature may differ from one dataset to another, and determining the optimum requires access to the labels. It is therefore unrealistic to use this individual temperature T * to compare methods, as it cannot be calculated for a new dataset without manually annotating a fraction of the data.
Calibration at the image level Generally, we observed that calibration (as measured by ECE) was negatively correlated with accuracy across models, for the 3 test datasets (Figure  2 ). ConvNext was the model providing the best overall performance. In particular, this model was slightly better in accuracy but much more efficient in terms of calibration (ECE of 2. 37%, more than 2 times less than the second-best model, Swin Transformer V2, which has an ECE of 5. 04%) on the Portugal dataset. In the meantime, the lightweight model, MobileNet, had bad to very bad (ECE of 34. 27% on the Portugal dataset) accuracy and calibration performances. Figure  2 : Scatterplot of ECE vs. accuracy values for five models (colored points) and three test data sets (panels), computed at the image level. Here, the ECE is not postcalibrated with temperature scaling (i.
e. the temperature is 1 for all models). As expected, temperature scaling allowed improving ECE values, for all models and datasets. We almost always observed a V-shape relationship between ECE and temperature, with ECE increasing quickly and by several percents around the optimum temperature value (Figure  3 ). This optimum temperature was generally greater than 1, suggesting that all models were initially overconfident to a greater or lesser extent. Interestingly, the V-shape curves of the different datasets overlapped well for the most accurate models (ConvNext and transformed-based models, ViT and Swin), and optimum temperature were similar across datasets. This suggested that a single optimum temperature would be sufficient to achieve efficient post-processing calibration. Indeed, using temperature scaling with temperature T , the models exhibited on average a relative reduction in ECE of 38% compared to without temperature scaling (T = 1) (dashed line in Figure  3 ).
Calibration at the sequence level Classification accuracy was much greater at the sequence level than at the image level (Figure  4  top). This was true for all models and all datasets, with up to +10% of accuracy for MobileNetV3 on the Portugal dataset. The Average Score and Average Logit were the two best methods for maximizing accuracy, with a slight advantage for the former. The gain in accuracy was however lower for models that were already efficient at the image level (ConvNext, ViT and SwinTransformer), but those remained the best models at the sequence level. Importantly, of the two aggregation methods that improved accuracy most, Average Score and Average Logit, only Average Logit provided well calibrated scores (Figure  4  bottom). The Average score was actually the worst aggregation method for calibration.
Therefore, considering both accuracy and calibration metrics, the Average Logit was the best aggregation method. Figure  4 : ∆Accuracy (top, the greater the better) and ECE (bottom, the lower the better) for the four aggregation methods (colored curves) and five models (x-axis) on three test data sets (3 panels). ∆Accuracy is the difference between the accuracy at the sequence level and the accuracy at the image level. Figure  5 : Curves of ECE values along the gradient of temperature values,for the four aggregation methods (columns), the five models (rows) and the three test datasets (colored curves). Light/gray area and dashed line defined as in Figure  3. We finally studied the interplay between temperature scaling and aggregation methods. We observed that the aforementioned V-shape was more flat for the Average Score method than for the other methods (first column in Figure  5  versus the others).
Discussion This study assessed the calibration of confidence scores, at image and sequence level, for different deep learning models in the context of species classification in camera trap data. Using five state-of-the-art models and three out-of-sample test datasets, we showed that score calibration can vary greatly across model architectures, in a way that is consistent across test sets. Further, we showed that the different aggregation methods to obtain scores at the sequence level gave very different calibration values, and that the Average Logit method must be preferred over the others for optimizing both accuracy and cal-ibration. Finally, we showed that temperature scaling can be used both at image level and sequence level, with a single temperature T that do not have to vary across datasets, to further improve the calibration.