dataesr/scientific-paragraphs-categorization huggingface dataset:
For each year, a classification accuracy report of the mentioned metrics could be obtained. In addition to the validation of the RF classifier each year using its corresponding terrain data, we also compared the reported accuracy of each year using the proposed methodology (S 2 IM) to the accuracy obtained when building a RF classifier using five-fold cross validation of the in situ data. This comparison will allow us evaluate the robustness of the proposed method against the classical training/validation methods that directly use terrain data for classification. For the irrigated plot, the σ 0 p value increased between two consecutive S1 images on 26 June 2019 and 01 July (∆σ 0 p = 1. 9 dB) whereas for the same SAR images, the σ 0 G value decreased by 2. 3 dB. Due to the important increase in σ 0 p and the important decrease in σ 0 G between two consecutive SAR images, the IEDM was capable of detecting the first irrigation event with a high irrigation-possibility weight on the irrigated plot on 01 July 2019.
Shaded regions represent the standard deviation of the average NDVI for selected samples for both irrigated (blue) and non-irrigated (red). Number of in situ plots refers to Table  1  whereas the number of selected plots refers to Table  2  for the irrigated and non-irrigated classes each year. Figure  8a -d of the four years shows that the selected irrigated plots had the same behavior of the NDVI as the in situ irrigated plots. The NDVI started to increase between April and May with approximately the same increasing gradient for both in situ and selected irrigated plots. The NDVI in both datasets (selected and in situ) then reached a maximum high NDVI value (approximately 0. 85) between July and August. Similarly, the selected non-irrigated plots showed the same NDVI pattern as the in situ non-irrigated plots for the years 2020, 2019, and 2018.
The NDVI values increased between April and May to reach a maximum value of approximately 0. 6 in July and August. Only in 2017 (Figure  8d ) did the NDVI temporal profile of the in situ non-irrigated plots look far different from the selected non-irrigated plots. In fact, in 2017, both irrigated and non-irrigated classes of the terrain in situ data show similar temporal profile with only small differences. In general, except for 2017, the selection criteria of by the S 2 IM produced a training dataset of irrigated and non-irrigated plots that were nearly similar in terms of NDVI profile to the dataset of irrigated and non-irrigated plots collected through a terrain campaign.
Random Forests Classification Results In this section, we present the results obtained by the S 2 IM for each RF classifier built at each year using the selected training data of our proposed methodology. Moreover, for each year, we compare the obtained S 2 IM results with RF 5-fold cross validation developed using the in situ terrain data (RF in situ). Table  3  summarizes the accuracy metrics obtained when applying the RF classifier, trained with selected training data of each year, on the in situ validation dataset of the same year. The validation of the RF classifier built from selected training data in 2020 generally shows very good accuracy (Table  3 ). The overall accuracy (OA) obtained using the S 2 IM reached 84. 3% with a similar F_score value (84. 1%). The irrigated class seems to have higher accuracy (86.
Using the selected training dataset in the S 2 IM, an overall accuracy of 81. 8% was obtained for the year 2018 with a weighted F_score of 82. 2% (Table  3 ). While the irrigated class showed good accuracy (F_score_irr = 86. 8%), the non-irrigated class showed lower accuracy than the irrigated class (70. 0%). However, in the 5-fold cross validation using in situ data, similar results were obtained. The F_score_nirr of the non-irrigated class (73. 6%) was lower than that of the irrigated class (92. 0%) while the overall accuracy reached 88% (6. 2% more than that obtained with the S 2 IM). In the RF 5-fold cross validation, the weighted F_score (86. 9%) was also slightly higher than that obtained using the S 2 IM (82. 2%). When validating the S 2 IM using in situ data in 2017, an overall accuracy of 72.
Method Generalization The effectiveness of the S 2 IM mainly resides in the ability to generate a training dataset each year. The training data generation helps in obtaining an irrigation map each year even with the absence of terrain campaigns for in situ data collection. On the other hand, it is known that when using machine-learning algorithms such as RF, the transfer of the model from one year to another remains difficult due to the variable response obtained using SAR and optical data between different years. However, to explore the difficulty of transferring the RF model from one year to another, we conducted an experiment to test the ability to transfer the RF classifier for mapping irrigated areas from one year to the other years. In this experiment, we built a RF model at each year using its own in situ data with S1 and optical data, and we applied it on the three other years to obtain the classification accuracies.
For example, a model was built using in situ data of 2017 (considered for training) and applied over the in situ data of 2018, 2019, and 2020 (considered for validation). Table  4  summarizes the weighted F_score obtained when applying an in situ-built RF model of one year on other years. Among different scenarios of training and validation for the four years, the maximum accuracy of the transfer does not exceed 68. 6%. All the models trained on one year and applied on the three other years presented low accuracy for mapping irrigated areas. Indeed, the F_score value ranged between 51. 5% and 68. 6% only. The results thus confirm the difficulty of mapping irrigated areas using only one-year in situ data and applying over several years. Therefore, as the irrigation mapping using spatiotemporal machine learning transfer was not yet achieved with high accuracy, the need for a training dataset for each year remains important.
Thresholds Sensitivity Analysis For the operational use of the S 2 IM method, it is important to discuss the threshold values of the irrigation metrics fixed to select training datasets (irrigated and non-irrigated classes). In order to show the flexibility of the thresholds considered, we conducted a sensitivity analysis to test the effect of changing the threshold values of the cumul ipw metric on the accuracy of the classification. For this experiment, we chose two years, one representing a humid year (2017) and the other representing a dry year (2019). The selection of the training dataset (step 1 of Figure  3 ) was re-performed using different threshold values for cumul ipw for both irrigated and non-irrigated classes. In fact, instead of considering cumul ipw ≤ 25 for non-irrigated class and cumul ipw ≥ 250 for the irrigated class, we considered new values in three different tests (Table  5 ).
In contrast, the F_score of 2017 decreased by 10% as the threshold values of the two classes became closer (≤100 and ≥175 for non-irrigated and irrigated classes respectively). However, for the threshold values ≤50 and ≥225, for non-irrigated and irrigated classes, respectively, both years showed the same accuracy as the initial thresholds considered in this study. The results could be analyzed using the histograms of the distribution of cumul ipw for in situ data presented in Figure  7  of Section 4. 2. 2. For the dry year of 2019 (Figure  7b ), the distribution of the cumul ipw for the in situ irrigated class is distinguished from that of the non-irrigated class where the separability between both classes is highly present using the cumul ipw. This distribution could be the same for the RPG data when selecting the training dataset.
For this reason, when narrowing the difference between the irrigated and non-irrigated threshold values from (≤25, ≥250) to (≤100, ≥175), both classes remain separable and distinct and thus the classification accuracy remained constant. However, for a humid year as in 2017, the cumul ipw distributions of the in situ irrigated and nonirrigated classes were closer to each other than 2019 (Figure  7d ). Thus, narrowing the threshold window from (≤25, ≥250) to (≤100, ≥175) increased the ambiguity between the two classes. When using the RPG data, the consideration of the thresholds (≤100, ≥175) most probably reduced the separability of the two classes in the selected training dataset (as shown for the in situ). As a result, the classification accuracy decreases significantly.
In fact, the RF can later detect irrigated plots with NDVI < 0. 8 if the radar signal shows significant increases in σ 0 p identified by the IDEM as potential irrigation events. However, for other terrains with irrigated plots corresponding to NDVI lower than 0. 6 (vegetables plots for example), a small number of irrigated plots may not be detected as being irrigated by the RF while the IEDM may show many irrigation events. For this reason, once the classification is completed, a filter could be applied to the non-irrigated plots, which consists in transforming a non-irrigated plot into an irrigated plot if the IEDM detects many irrigation events with high certainty. For our database in Orlèans, the improvement of the mapping accuracy after applying this filter is not more than 1%. This improvement could be higher in another territory with irrigated crop types with low NDVI values (0.
Irrigation Mapping in Humid and Dry Areas Over our study site, four different years were examined. One of the years (2017) was characterized by a very humid summer while another (2019) was characterized by a very dry summer. As shown in the results, using either our approach (the S 2 IM) or an RF classifier directly performed using in situ data, the irrigation mapping in humid conditions is less accurate than that in dry conditions. Using either in situ RF or the S 2 IM, the irrigation classification accuracy in 2017 was between 73% and 78%. The decrease in the classifier performance is mainly due to the minimized difference between irrigated and non-irrigated plots in both SAR and optical (NDVI) data for humid conditions. In fact, this is considered as one of the limitations for using remote sensing data (nowadays S1 and S2 data) in irrigation mapping in humid areas.
Conclusions In this study, an operational methodology for mapping irrigated areas at plot scale (S 2 IM) has been proposed. To address the main issue related to the dependency of supervised classification models on in situ terrain campaigns for irrigation mapping, the methodology presented in this study is capable of automatically generating reference dataset of irrigated and non-irrigated plots to be used in a supervised classification model. The reference data selection was based on two metrics derived from S1 and S2 temporal series. The RF classifier was then used to map irrigated areas using the selected reference dataset, S1 and S2 data. The method was applied on a study site located in northcentral France for four years between 2017 and 2020. The proposed methodology "S 2 IM" delivered reasonable performance with overall accuracy between 93.
Acknowledgments: Authors wish to thank the  French Space Study Center (CNES ,  TOSCA 2021 ), the  National Research Institute for Agriculture, Food, and Environment (INRAE) , and the  Mediterranean Agronomic Institute of Montpellier (CIHEAM, IAMM). The authors wish to thank the  Occitanie region of France  for supporting this work. The authors wish also to thank the  European Space Agency (ESA)  for the Sentinel 1 and Sentinel 2 data and Theia pole for the calibration of the Sentinel-2 images. Finally, the authors would like to thank the  Regional Directorate of Food, Agriculture , and  Forest of (D. R. A. A. F) of the "Centre Val de Loire " and particularly  Gérard Guillaume  ( DRAAF Centre Val de Loire/SSI ),  Audrey Oddos  ( DRAAF Centre Val de Loire/SRISE ), and  Claudie Suzanne  ( DRAAF Centre Val de Loire/SSI ) for the collection of field data and their expertise.
, 2011). To investigate the time series data generated by this approach, we use changepoint-modelling  (Gallagher et al. , 2011). This allows for the inference of statistically significant shifts in the dataset, meaning we can better investigate the drivers of change. To investigate the impact of past climate and moisture variability on the CARs of individual sites in discreet periods, we calculate average CARs for three periods of both climatic and human-impact interest: the Roman Warm Period (RWP; 2200 -1550 yr BP), the Medieval Warm Period (MWP; 950 -1250 CE) and the Little Ice Age (LIA; 1300 -1850 CE). We also calculated the apparent recent rate of carbon accumulation (RERCA) for the period from 50 yr BP to present (Table  1 ). However, the RERCA values should be treated with caution because CAR values in the uppermost layers of peat may vary due to the exponential nature of peat decay  (Clymo et al.
, 1998; Young et al. , 2019). Therefore, we also quantified the amount of carbon accumulation for the past 1000 years (see Table  2  for all average values), following Gallego-Sala et al. (2018). To estimate the total size of peatland carbon sink in the Romanian Carpathians selected due to the availability of data survey on peatland extent, we used the mean carbon content and the mean bulk density from the 8 peat bogs, and total bog area and bog depth from  Pop (1960) , and applied Equation 1 to these data. To account for uncertainties, we used a 1-SD for our new data, and applied a 10% error to  Pop (1960)  prior to estimating the peatland carbon sink with Monte Carlo simulations (Supplementary Table  2 ). Repeat Monte Carlo simulations (n=10,000) were employed to estimate each of the variables, using the R package truncnorm, and boundaries dictated by the data.
Composite CAR record and data-model comparison To interpret the effect of past hydroclimatic drivers on the Romanian Carpathian mountain peatlands, we synthesise all datasets, resulting in a representative record of CAR across the Middle and Late Holocene in the region (Fig. 3 ). To achieve this, for each individual site, we bin all data at 50-year intervals. For these bins, we consider the 25 years either side of the bin age (i. e. , the 0 yr BP bin covers the period 26 to -25 yr BP). To allow for comparison across sites, we convert these raw binned CAR data into z-scores. We then calculate mean and standard deviation values for each of the bins, for all studied sites. This approach allows for the interpretation of large-scale changes in CAR for a region across a long time period. As individual CARs are highly variable, with numerous stochastic and site-specific forcing factors, averaging multiple records provide insights into broad scale factors controlling regional CARs  (Loisel et al.
, 2021; Marlon et al. , 2016). To compare our composite CAR output to modelled palaeoclimatic indices, we use data from PaleoView  (Fordham et al. , 2017). From PaleoView, we extracted 6 parameters related to past precipitation and temperature variability, with each temperature variable expressed as a z-score relative to recent (i. e. , present day set as 1975 CE, or -25 yr BP in the model). For each variable, we use the mean area extracted from the region 40° -47. 5 °N, 17. 5° -27. 5 °E, which covers all sites in this study (Fig. 1 ). To investigate the effect of other potential drivers, we compare the composite output to three measures of human impact and land use (population, area of pasture and area of cropland) derived from the HYDE dataset  (Goldewijk et al. , 2017). Due to the nature of the dataset, we use the "Central Europe" region, as it contains both Romania and Serbia  (Goldewijk et al.
, 2017). We also explore an estimate of the intensity of land use in the region, from  Kaplan et al. , (2011) , which represents a proportion of the 5 degree grid square (centred on 46 °N, 24 °E) utilised for any type of human activity on the landscape. To investigate the impact of dust deposition, we used two regionally representative reconstructions of dust deposition through the Holocene, from MOH  (Longman et al. , 2017b) , and TM  (Panait et al. , 2019). To investigate the impact of biomass burning on the record, we compare with the compilation of eastern European charcoal deposition from  Feurdean et al. (2020) , from which we extract the boreal (BOR) record, as this covers all sites in this study. We used linear interpolation to bring all datasets onto a 50-year timescale for comparison with our binned composite CAR record.
Data-model comparison One way in which potential drivers of change in peat accumulation may be elucidated is by comparing our data to model reconstructions of climatic variables such as temperature and precipitation, and investigating their relationship with the observed shifts in CAR (Fig. 4 ). Here we compare the results of our composite record (binned at 50-year resolution) to a number of climatic indices extracted at 50-year timesteps from modelling reconstructions of the last 5000 years  (Fordham et al. , 2017). When comparing the full datasets, several statistically significant correlations are revealed (Table  3 , Fig. 4 ). These include both temperature and precipitation seasonality, and diurnal and annual temperature range (Table  3 ). Strong negative correlations between both precipitation and temperature seasonality and CARs indicate that as seasonality increases, CARs decrease.
, 2020; Wang and Moore, 2014). This may explain the lack of strong correlation between our CAR composite and two regionally representative dust flux records (Table  4 ). We showed that both dust input and variability in hydroclimate may have played a role in controlling CARs in the study region, but we hypothesise that human impact may have become the major controlling factor after 3000 yr BP. To investigate this, we compare the CAR composite record with several land use and human impact-related proxies from the HYDE dataset (Fig. 6 ). We focus on the last 2000 years of the record due to data availability, as prior to 2000 yr BP, only a single data point for every thousand years is available from the HYDE dataset. However, these data are sufficient to indicate the strong positive correlations between population and land use, and CARs in central-eastern Europe (Table  4 ).
This is further supported by the comparison with land use as reconstructed by  Kaplan et al. (2011) , and the strong positive correlation between the two datasets (Fig. 6 , Table  4 ). To supplement this, we also compare to a synthesis of eastern European biomass burning  (Feurdean et al. , 2020). In the studied period, it appears biomass burning was controlled primarily by human-driven tree felling and burning, and so may be used to estimate levels of land exploitation  (Feurdean et al. , 2020). There are strong positive correlations between CARs and biomass burning in the last 2000 years (Fig. 6 , Table  4 ). This finding suggests biomass burning and associated forest removal has a positive impact on central and eastern European peatland CARs. As described earlier, increased human impact (logging, use of fire) lead to reduced forest cover  (Feurdean et al.
Conclusions Using nine peatbog records from the Carpathians, we compiled a detailed record of changing carbon accumulation rates (CARs) through the Holocene. Our data indicates that CARs for bogs located in the Carpathian Mountains are potentially very efficient carbon sinks, with the long-term apparent carbon accumulation rates amongst the highest in the published literature, and comparable to high mountain bogs in South America. As a result, the peatland carbon sink in the Carpathians and throughout central-eastern Europe may be large, despite the small area of peatland coverage. Our work suggests that mountain bogs should be considered in future estimates of the peatland carbon sink, and that further studies are needed to refine estimates of carbon accumulation in such environments. From our reconstructions, CARs varied throughout the Holocene, with high rates recorded in the early Holocene (10000 -7000 yr BP), before a slow decline culminating in CAR nadir around 4000 yr BP.
Acknowledgements We are indebted to  S. B. Marković ,  N. Tomić ,  I. Obreht  and  Sz. Kelemen  for fieldwork support. This work was supported by a grant of the  Romanian Ministry of Education and Research, CNCS -UEFISCDI , project number  PN-III-P4-ID-PCE-2020-0914 , within  PNCDI III.
Through IWC-SORP, the Acoustic Trends Project working group has built upon the pioneering work done in the first decade of the twenty-first century, and has fostered an increasing number of passive acoustic studies focusing on the calls of Antarctic blue whales and to a lesser extent fin whales across broad time and spatial scales, as well as acoustic data processing and analysis methodology. The data from these studies have been collected both from ships during Antarctic voyages and from long-term moored recording devices  4, [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] [31]. The project working group presently (as of March 2020) has access to more than 300,000 h of passive acoustic data that have been collected throughout the Southern Ocean over the past 20 years.
Automated detection of whale sounds. The volume of existing and incoming acoustic data far exceeds the capacity of human expert analysts to manually inspect it, and as a result automated algorithms have been relied upon to determine the presence of sounds from marine mammals in the recordings. Ecological results from long-term analyses have been reported in the form of presence (e. g. months, days, or hours of recordings with call presence), or as estimates of call numbers per time-period (e. g. see studies listed in Table  1 ). However, the results are not easily comparable because different studies had different data collection protocols and employed different analytical techniques, neither of which have been standardised. Furthermore, robust measures of bias and variability, which can be dataset-specific, are not always reported alongside results (Table  1 ).
A variety of automatic detection algorithms have been used to detect the calls of blue whales and fin whales. Algorithms to detect stereotyped calls of these species include matched filters  [32] [33] [34]  , energy detectors  35  , subspace projection detectors (blue whales only  26, 36  ). However, the most widely used algorithm has been spectrogram correlation  37  , and this has been implemented in a variety of software packages  [38] [39] [40]  and has been used widely on a variety of different datasets  [16] [17] [18] [19] [20] 30, [41] [42] [43] [44]. Spectrog am correlation is similar to matched filtering except that it acts on the spectrogram, rather than purely in the time or frequency domains; instead of cross-correlating a time series or spectrum, it correlates an image template or kernel pixel-by-pixel with the spectrographic data of interest.
On top of the well documented decreases in tonal frequency of sounds from year-to-year, there are predictable intra-annual changes that have also been observed  14, 48, 50. There is some evidence that the properties of fin whale sounds vary geographically in the Antarctic  15, 18  , and have been found to vary temporally in other oceans  33, 51, 52. While these changes may seem small and/or occur over long time periods, they must nevertheless be accounted for when using automated detection algorithms to detect trends in long-term and widely dispersed datasets  53. Lastly, the acoustic recordings around the Antarctic have been made with a variety of instruments. These include: Scripps Acoustic Recording Packages (ARP); Multi-Electronique Autonomous Underwater Recorder for Acoustic Listening (AURAL); Australian Antarctic Division Moored Acoustic Recorders (AAD-MAR), Develogic Sono.