dataesr/scientific-paragraphs-categorization huggingface dataset:
This ensured that we avoided confusing these objects with lines contained in the surveyed FFS, such as parcel boundaries. (2) We then used a regional landcover database covering the Grand-Est (CIGAL, https://data. geocatalogue. fr/id/dataset/FR-236700019-BdOCS20112012-CIGAL-V2) region to remove negative examples located on water or in artificial areas (buildings or roads). For the learning set, and based on our expertise, we assumed that it was impossible to observe the surveyed archaeological forms in these areas. (3) Finally, negative tiles located in the first-order neighbourhood of positive tiles were removed, as they likely shared similar features characteristic of positive tiles partially covered by FFS. After these steps, we had 44 612 TS (6565 positive and 38 047 negative) and 38 266 RaF tiles (295 positive and 37 971 negative) in each training set.
Training detection models and classifying FFS We compared two classification models for detecting FFS. First, we trained a RF model due to numerous reasons. (1) The parameters are in a rather low number, and are also easy to understand. Thus, specialists such as archaeologists or historical ecologists can readily use the detection model. (  2 ) RF offers the opportunity to measure the importance of input features variables. Under certain circumstances, this would help identify variables with low contribution, thus allowing training and prediction to run faster. (3) RF is able to manage missing data in case of an incomplete coverage for example. (4) RF offers a good comparative basis to deep learning models because of its ability to properly learn information with limited training datasets, as is often the case in an archaeological context  (Breiman, 2001).
This would more likely not be the case with CNNs, due to the limited dataset. FCN also offers the opportunity to use pre-computed features such as LBP. Therefore, we made the assumption that combining LBP features and the SMoTE technique (see Section 2. 4. 2) was an effective solution for increasing the training set and tackling issues caused by the unbalanced nature of the data. For the FCN, we first optimised the hyperparameters using a random search strategy. We searched only for the number of hidden layers and neurons (Table  2 ). In addition to inputs and outputs, the final model consisted of four hidden layers, containing 20, 15, 10, and 5 neurons, respectively. A rectified linear unit (ReLU) was used as the activation function for each layer. To improve model performance during the learning process, we used batch normalisation to normalise the output of each layer.
This was notably the case for the RaF data set where we observed a recall score decrease of approximately 0. 41 points for both models. It should also be noted that the average recall score obtained with FCN for RaF detection without post-processing was equal to 1. All positive validation tiles were classified correctly at the expense of a very low precision score. We also studied the variable importance ranking, measured by the mean decrease in accuracy (MDA) for the 30 most important variables of the RF model (Fig. 6 ). The most important variable for classifying TS was mean slope followed by LBP bins, such as u 1 29 and uu 1 1. Similar metrics from the second hillshade component were also ranked sixth and seventh (i. e. , u 2 29 and u 2 2). The latter features correspond to the edge-like regions of TS, indicating that LBP are particularly sensitive to repetitive-oriented parcel boundaries.
Prediction results over the entire study site Large-scale results for the TS and RaF data sets are shown in Table  3. We focus here on the results for detecting positive tiles to examine the ability of the developed processing chain to retrieve objects of interest over large territories. Furthermore, we also focus on the positive tiles to increase the transparency of our results and facilitate comparison with similar previous or future findings (Fig. 7 ). The classification results discussed here were obtained using the best-performing RF and FCN models selected after the cross-validation stage. Classification results showed that 4973 of 6565 TS tiles (75%) were identified by FCN with 1289 (20%) false positives. We observed, however, a more robust performance for the RF model with 5880 tiles (89%) assigned to the TS class.
8. Both RF and FCN models provided very similar spatial patterns to the references for the TS data set (SSIM RF/Reference = 0. 98, SSIM FCN/Reference = 0. 95). Similarities between the reference spatial patterns and the model predictions were lower for the RaF dataset but were nonetheless satisfactory (SSIM RF/Reference = 0. 83, SSIM FCN/Reference = 0. 75). These statistical maps also showed that FCN overdetected tiles marked by a higher density of squares containing a low number of tiles (between 1 and 5) than in the reference map (see histograms). Although these errors are of note, it is also important to mention that FCN exhibited a better generalisation power than RF. This was particularly visible for TS in the south-eastern area of the study site, where slope gradients are low and differ from areas that contain most known sites.
For these latter areas, FCN correctly detected more tiles than RF, indicating a greater ability to predict TS within variable topographic conditions, less represented in the learning set. For RaF, both models detected tiles corresponding to major high-density zones observed in the reference layer. However, overdetected tiles were particularly numerous with FCN in the extreme eastern part of the study site. After visual inspection, these errors were instructive because these areas corresponded to actual RaF sites that were undetected during the ground-truthing stage. Thus, this result may highlight the strong ability of FCN to survey unseen data and its low sensitivity to annotation errors. Detection results reported at the scale of the FFS sites showed similar results to the classification results derived from the window scale.
Known FFS tiles In particular, our approach reproduced larger trends in the landscape with a high level of similarity between the predicted and reference spatial patterns of FFS. These results are in line with the recommendations of  (Cowley, 2012) , who emphasised the importance of providing large-scale maps for identifying the key structures of surveyed forms with reasonable areas of uncertainty due to the complexity of the investigated landscapes. Freely distributed ALS data, such as RGE-Alti, are sufficiently adapted to the needs of detecting archaeological sites. These data were initially acquired for different purposes than archaeological ones, such as forestry or water monitoring, and their quality could be questioned, namely due to moderate pulse densities and/or insufficient spatial resolution.
Probability gradients allow for the rapid identification of tiles of interest, with a high level of certainty, to direct specific field investigations, such as archaeological dating as well as topographical and ecological surveys. This approach can also provide more nuanced maps for landscape archaeologists and ecologists, then combining them with external data (excavation and soil data) to help to confirm the presence of these landforms. Regarding the two surveyed FFS, cross-validation results initially demonstrated better performances in the detection of TS compared with RaF. Despite a larger intraclass heterogeneity among TS, these results were expected because of the low number of known positive examples in the RaF training set. The small amount of training data is a wellidentified issue in archaeological site detection  (Trier et al.
We also strongly believe that efforts to increase archaeological datasets should be pursued in three lines of works: (1) to keep on developing citizen web platforms in order to increase labelization of archaeological forms (see https://www. globalxplorer. org/); (2) to keep acquiring ALS data on vast territories in order to conduct large-scale non-intrusive archaeological surveys and to obtain uniform image datasets; (3) to keep developing GIS approaches to align existing ALS-derived data (co-registration, disaggregation, etc. ) and to maximise the amount of available sources. In addition to these steps, several precautions also be taken. In the RaF configuration specifically, the SMoTE technique might also explain a part of classification errors. Because of a very low number of positive samples and the random sampling, the data space in which additional positive samples are calculated may be underestimated.
Consequently, new samples increase within a very restricted data range that does not cover the full range of positive samples. We thus suggest evaluating the use of SMoTE in archaeological surveys regardless of the selected classification model (i. e. , early ML or DL models). Nevertheless, the collaboration between landscape archaeologists, ecologists, and remote sensing specialists is required to increase the number of known positive sites across different geographic regions and to fully capture all possible configurations. Another solution is to increase the number of examples by calculating features of similar actual landscape forms  (Cowley et al. , 2019). For instance, it may be useful to calculate LBP metrics on aerial greyscale photographs, including parcels networks, to complete the RaF training set.
For the classifiers, RF outperformed FCN with a higher F-score obtained for the TS data set. This was confirmed by the accuracy assessment reported for the entire study area. Particularly, the false-positive rate with RF was substantially lower than that of FCN. These results corroborate previous findings, which showed strong RF performance when the training data are well represented  (Menze and Ur, 2012; Guyot et al. , 2018) ; however, these results should be nuanced. First, we applied an 80%-20% split to fully capture trends in the training set. Therefore, Fig. 9. Example of TS probability map generated through the properties of the FCN model decision process. P. -A. Herrault et al. classification results presented for the full data set are only based on 20% independent tiles. This could overestimate accuracy indicators, as the latter model is known to fit the training data very well.
, 1998; Foster et al. , 2003; Powers, 2004; Niklasson and Granström, 2000) , opportunistic historical datasets  (Bommarco et al. , 2014; Jamin et al. , 2020) , and local ALS data  (Dupouey et al. , 2002; Georges-Leroy and Montes, 2013). Consequently, conclusions are often spatially limited. The present study addresses these limitations by offering the possibility to assess a new spatiotemporal baseline and detect potential time lags over a longer period at a regional level (Fig. 11 ). More specifically, several ecosystem studies might benefit from this approach. For instance, the potential effects of former land use on forest plant communities could be explored at a regional level. Given those plant communities that develop after afforestation of abandoned lands are likely to differ, it is interesting to evaluate similarities between plant composition in forests masking FFS (or not).
Our approach would allow identifying several groups of forests characterised by different histories (Fig. 11 ), whereas forest plant composition (taxonomy and functionality) might be assessed by exploring a free, large-scale botanical data set (Vigie Flore, TeleBotanica, TRY database). At this scale, the low granularity of free botanical surveys would not be an obstacle, and model outcomes would offer the possibility of verifying the existence of time lags in forest plant composition at an unprecedented spatial scale.
Conclusion We developed a processing chain to detect Medieval Terraced Slopes and Ridges and Furrows in the Vosges mountains using an ALS-derived DEM. Our results demonstrated the high suitability of our approach for detecting FFS at a large scale and for reproducing major trends in the landscape. The proposed development relied on three specific contributions: (1) the calculation of LBP helped describe FFS efficiently, thus providing proper features for the training of detection models; (2) LBP features associated with the SMoTE technique proved to be a powerful combination for processing unbalanced and small limited archaeological datasets; (3) FCN are promising detection models for non-intrusive archaeological surveys at a large scale. Their simple architecture offers a certain flexibility and they showed a low sensitivity to annotation errors.
The farm-level optimization algorithm used in these models simulates new combinations of activities that maximize a given objective function and are consistent with specified biological, technical, structural, and regulatory constraints. The farm model Orfee  (Mosnier et al. , 2017)  is particularly suited to the configuration of mixed production systems since it takes into account several complementarities between the different farm enterprises. These complementarities are parameterized according to current knowledge regarding the value of organic fertilization, impacts of one crop on the following ones, on-farm production of feed and litter for animals, value of grazed grass in mixed grazed pasture, possibility to use machines, building and labour for different productions. It also benefits from references from a large database of commercial farms (INOSYS:  Charroin et al.
Acknowledgements Additional funding was obtained through the  PSDR4  project new-DEAL co-funded by  INRA, Irstea  and the  Auvergne-Rhône-Alpes region. The authors thank  Michel Lherm  ( Inrae ) for his compilation of CAP changes,  Charlène Verdier ,  Inosys-Réseaux d'Élevage and Inrae-Umrh  for their production of technical and economic references on livestock farms.
This results in inconsistencies in the backscattered pressure levels along the MBES fan. Without adequate corrections, these inconsistencies would lead to erroneous values of the Volume Backscattering Strength S v , from which the mass concentration is derived  [16]. The use of MBES as a SPM monitoring tool thus relies on the accurate calibration of each individual beam of the MBES fan, which in practice requires significant resources (financial, material, human)  [46, 47]. In addition, because it is a single-frequency instrument, the acoustic inversion of MBES data is limited by the prior determination of a mean radius or an Equivalent Spherical Radius (ESR) and by the choice of a backscattering model appropriately describing the acoustical properties of the suspension. In January 2015, time series of raw Kongsberg EM3002 MBES data at 300 kHz were acquired in the macrotidal estuary of the Aulne River (France).
The sphere was slowly moved back and forth across the beam of interest, as placing the sphere exactly inside the selected beam was unpractical. The same acquisition parameters (4 Hz ping rate, 0. 15 ms pulse length) and processing method (beamforming) are applied for calibration and experimental data, so that the calibration analysis is consistent with the study site dataset. During the calibration measurements, the beam insonifying the target sphere was at h c = 3°. A set of >1200 pings was recorded, containing a total of four, slow back and forth crossings of the sphere within the beam of interest. After correcting from spherical spreading and attenuation loss, the object is tracked on the acoustic data. Then the TS values observed by the echosounder are averaged. Finally, the calibration constant is obtained from the difference between the TS measured by the beam under calibration and the theoretical TS of the sphere.
Even at high frequencies (>0. 5 MHz), no noticeable sediment attenuation could be observed. So, attenuation was neglected during the processing of the acoustical data (ABS and MBES). To generate vertical profiles of acoustic backscatter, a mean, measured acoustic backscattered level was obtained for each ping by averaging over the 100 sampling cells centred around 0. 5 m, with a pulse length of 6. 67 ms. Backscattered profile values were then converted into volume backscattering coefficients s v (related to the volume backscattering strength as S v ¼ 10log 10 ðs v Þ). Then, the data were inverted using the Non-Negative Least Square algorithm NNLS  [59] , yielding a numerical density or mass concentration estimate for each input ESR class (16 log-spaced classes ranging from a m ¼ 30 lm to a M ¼ 500 lm)  [49].
MBES backscattered intensity inversion results Every 81 beam file of the MBES, containing >60,000 pings, was inverted following the steps described in section 3. 3. The backscattering cross-section r bs used for the inversion is the same as for the ABS data. Fig. 6  represents the inverted time series MBES signal for the central beam along the water column, completed with a comparison between two inverted beams at 0°and 30°and the in situ observations at a fixed depth of 7. 75 m. The blanks in the MBES data (Fig. 6 ) are the results of sudden system failures (eg. ~9:40 AM) that occurred regularly as the SCSI disk stopped recording the raw data. These time-lapses thus correspond to reboots of the system. A first visual inspection of Fig. 6a  reveals that the estimated mass concentration continuously grows from the beginning of the experiment (beginning of the ebb, when concentrations were of the order of 50 mg/L) up to approximately 9:30 am.
Acknowledgments This work was conducted in the framework of the  ANR   EPURE  project (Grant  11 CEPL 005 02 ). This work was also supported by the  Laboratoire d'Excellence LabexMER  ( ANR-10-LABX-19 ) and co-funded by a grant from the  French government  under the  program Investissements d'Avenir. Contributions from  Romain Cancouët ,  Christophe Martin ,  Olivier Blanpain ,  Susanne Moskalsky ,  Paul Juby  and  Marcaurelio Franzetti  were key to the success of the field measurements. The authors thank the  Direction Générale de l'Armement (DGA) , the  Centre National de le Recherche Scientifique (CNRS) , and l' Université de Bretagne Occidentale (UBO)  for funding G. F. 's doctorate. The authors would also like to thank the  Institut de la Recherche pour le Développement (IRD)  for the use of their instruments and datasets, and for the help and support of  IRD  staff both in the office and at sea.
Of the 4,401 BC cases, a total of 3,924 (89%) cases had stage information. Information on the grade of differentiation and histological subtype at diagnosis was collected based on pathological reports and available for 3,433 (78%) and 4,120 (93. 6%) BC cases, respectively. Data on established BC risk factors and other potential confounding factors were obtained from the E3N self-administered questionnaires at baseline. Information collected at baseline on smoking, anthropometry (height, weight), physical activity, diabetes, hypertension, benign breast disease, gynecological follow-up, family history of BC (FHBC), education, age at menarche and at menopause, use of exogenous hormones, number of children, age at rst full-term pregnancy (AFP), and breastfeeding  [31]. Follow-up questionnaires were sent every 2-3 years thereafter.
Assessment of long-term exposure to airborne cadmium The method employed to estimate airborne cadmium exposure at the individual residential address level have has been previously described in detail  [33] [34] [35]  and applied in two previous studies  [27, 36]. Brie y, the residential history of the women, from their enrolment in the E3N cohort until the index date (BC diagnosis for cases, date of diagnosis of the case in the case-control pair for controls) was used to estimate atmospheric exposure to cadmium, within a Geographic Information System (GIS). A detailed retrospective inventory of industrial cadmium emitting sources over the entire metropolitan France between 1990 and 2008 was performed  [33]. Sources of emissions were assessed using emission factors from the OMINEA (Organization and Methods of the National Inventories of the Atmospheric Releases in France)  [37]  and the EMEP (European Monitoring and Evaluation Program) [38] databases.
Overall, 2,700 cadmium sources were inventoried over the French national territory from 1990 to 2008  [33]. The participants residential history from 1990 to 2008 and inventoried cadmium emitting sources were geocoded (X and Y coordinates, addresses) using the ArcGIS Software (ArcGIS Locator version 10. 0, Environmental System Research Institute -ESRI, Redlands, CA, USA) and its reference street network database, BD Adresse®, from the National Geographic Institute (IGN)  [35]. To classify the study subjects according to their airborne cadmium exposure, a GIS-based metric was developed and calibrated using a set of parameters (local meteorological data, characteristics of industrial sources, e. g. emission intensity and stack height)  [34]. Speci cally, the annual airborne cadmium exposure index (AACEI) was estimated using the following GIS-based metric: where j was the place of residence (j=1,…,J); i was the industrial source (i=1,…,I), EI was the source annual cadmium emission intensity (in kg/year); t was the emission period duration (in year); d was the residence-to-source distance (in m); Fi was the factor accounting for the weighted contribution of wind direction from the industrial source i to the participant's residence j; h i was the stack height (in m); h median was the median value of the other sources' stack height (in m) in a 10 km buffer, and was taken into account only when h i was greater than 90 m.
Since there was no difference in the OR estimates between the two models, we only reported the results of the fully adjusted models in the main manuscript. For contraceptive and menopausal MHT variables, we considered the values collected in the last questionnaire before the date of diagnosis in cases, whereas all other adjustment variables were taken at E3N baseline questionnaire. For covariates with less than 5% missing data, the latter were imputed by their modal or median value of the control population; and for variables with more than 5% of missing data (only alcohol intake and rural urban status at birth), a category of missing data was created. Statistical analyses for quintiles of the cumulative airborne cadmium exposure index were performed by stage, grade of differentiation, and histological subtype of BC at diagnosis using the rst quintile as the reference value.
This comparison allowed us to evaluate the uncertainty estimates for the lidars and determine if we are realistically estimating the measurement uncertainty in our instruments and the total uncertainty in our profiles of ozone and temperature. We found two outstanding issues during this exercise: (1) the temperature uncertainty budget for the LiO 3 S lidar overestimates the uncertainty above 35 km, and (2) there was a previously undetected discrepancy between the temperature uncertainty budget for the French LTA and NASA of up to 2 K below 50 km. In response to the LAVANDE campaign findings, the photomultiplier tubes (PMTs) for the low-gain channels (< 50 km) in LTA were replaced and plans were made to modify filtering codes for LiO 3 S temperatures for eventual submission to the NDACC database.
Microwave Limb Sounder The MLS uses a spectrometer to make limb measurements of thermal microwave radiation of the atmosphere. The instrument, aboard the Aura satellite, allows for the retrieval of stratospheric ozone profiles with a vertical resolution of about 3 km. Measurements of stratospheric temperature profiles are also made with a typical vertical resolution of 8 km at 30 km altitude, 9 km at 45 km altitude, and 14 km at 80 km (full width at half maximum (FWHM) of the averaging kernels;  Schwartz et al. , 2008). MLS profiles of temperature, geopotential height, and ozone were extracted from the version 4. 0 MLS dataset. A more complete description of the instrument is given in  Waters et al. (2006). For the HOPS campaign, the geopotential altitude is converted to a geometric altitude and regridded to allow for a direct comparison with the lidars and sondes.