dataesr/scientific-paragraphs-categorization huggingface dataset:
To characterize inmates by their mental health history, we leverage our rich socio-demographic data to predict the probability of having any mental health visit within 5 years prior to the crime. 44  As shown in Table  4 , Columns (1)-(  2 ), there are no significant differences neither on the extensive (Panel A) or intensive (Panel B) margin between those predicted to be below and above median mental health visits pre-event. Keeping in mind the caveats of splitting the sample by pre-event outcome in a TWFE estimation, we also assessed this type of heterogeneity using inmates' actual mental health history. We define as defendants with no mental health history those with zero mental health visits in 2006 (56% of the sample), and as defendants with a mental health history those with a positive number of mental health visits in 2006.
This will hold distance from treatment constant but the outcome will then be measured in different years for treatment and controls. We chose to present results here for the former, however, results for the latter go in the same direction. number of mental health visits but the differences are not statistically different. This set of results suggests that our findings are not mainly driven by inmates being discouraged to utilize services they accessed pre-prison. Prison may also safeguard against onset of new mental health disorders. Rehabilitation and prison conditions. To assess additional mechanisms, we leverage the richness of our data to explore heterogeneity in the effects of incarceration on mental health along dimensions capturing differences in rehabilitation programs across prisons.
While noting that we cannot interpret these results causally, as the decision to stay on with the incarcerated defendant post prison is potentially endogenous, we view these findings as suggestive of the "removal" mechanism. To shed further light on the broader impacts on family and child well-being, we now utilize additional data on yearly child protection services (CPS) incident reports. Specifically, we measure two types of CPS-related events at the annual level for families with incarcerated defendants. First, we observe all events of foster care provided to a child at high risk of neglect or abuse. We consider whether at least one child in the defendant's family was taken out of the family residence by the CPS and provided with alternative shelter following a transfer of custodial rights.
[36]  employed a comparable approach by assimilating coarse-scale SSM data derived from SMOS products, disaggregated to a 1 km resolution, into a basic land surface model driven solely by precipitation. In this method, irrigated areas were identified with satisfactory accuracy by examining the cumulative analysis increment, which represents the difference between the analyzed and background SSM values throughout the season. Similarly, Zaussinger et al. [22]  employed various coarse-resolution datasets and compared them with a water balance driven by the MERRA-2 model in order to assess the utilization of irrigation water. The examination conducted at a statewide level yielded divergent findings (with correlation coefficients ranging from 0. 36 to 0. 8) and notably lower estimations compared with the documented irrigation water withdrawals.
Among various SAR satellite constellations, the Sentinel-1 (S1) SAR constellation (S1A and S1B) stands out as an effective tool for mapping and monitoring large-scale irrigated areas. This is attributed to its unique combination of a high revisit time (6-day interval) and high spatial resolution (pixel spacing of 10 m × 10 m). Benabdelouhab et al. [41]  demonstrated that C-band S1 SAR data can effectively detect irrigation activities in irrigated wheat plots with a three-day interval between irrigation and SAR acquisition. Detecting consecutive irrigation events requires a comprehensive multi-temporal dataset due to the dynamic nature of irrigation. In a recent study by Bazzi et al. [17] , a near real-time approach was developed to determine irrigation timing in diverse summer and winter crop fields located in France and Spain.
First, Sentinel-1, Sentinel-2, weather, and irrigation data were collected and then SSM S1 values were estimated over maize plots using S 2 MP. Second, the rate of change of SSM S1 was computed between subsequent S1 acquisition dates for ascending and descending orbits separately. Then, irrigation combinations of dates and amounts were entered into Optirrig in order to detect the irrigation combination that led to a rate of change that matched that of SSM S1. The performance of the proposed method for detecting irrigation was evaluated by comparing the actual irrigation dates and amounts applied in the studied maize fields with those estimated.
Site Management and Irrigation Datasets Table  2  provides an overview of the total number of irrigation events, irrigation method employed, and sowing and harvest dates for each plot. In Montpellier, the P1 plot, which spanned an area of 13,860 m 2 , had a deep soil of colluvial-alluvial origin with a silty clay loam texture (approximately 24% clay, 44% loam, and 32% sand), contributing to its high-water-holding capacity  [86]. Near Tarbes, the P2, P3, and P4 plots, which encompassed a total area of 10,490 m 2 , had a silt loam texture characterized by high fertility and effective drainage capacity  [87]. Irrigation typically occurred during the dry summer season, which extends from June to October in Montpellier and from the beginning of July to the end of August of each year in Tarbes. In this study, the sprinkler irrigation method was employed over the reference plots.
It assumed no nitrogen stress due to sufficient fertilization on the study sites, and hence, the nitrogen cycle component was excluded from the model. Within its inner hydro-agronomic module, Optirrig conducts a daily water balance, considering factors such as infiltration, drainage, evaporation, transpiration, and changes in soil water reserves (R1, R2, R3) in different reservoirs (surface, root zone, and deep). Optirrig relies on four daily climatic inputs: precipitation (P), mean air temperature (T), potential evapotranspiration (ETo), and total global solar radiation (R g ). Additionally, irrigation (I) can be incorporated as a management parameter. Crop growth is tracked through the leaf area index (LAI), which is determined by the thermal time (TT) and potential water stress (S w ), as shown in Figure  2.
The crop coefficient (Kc) and partition coefficient (Cp) are derived from the LAI, as they control the allocation of ETo into transpiration demand (Tp 0 ) and evaporation demand (Es 0 ). The actual transpiration (Tp) and evaporation (Es) are then calculated based on available water reserves, which are continually updated. Biomass accumulation (TDM) is determined by radiation interception and can also be influenced by water stresses. Finally, crop yield (Y) is evaluated using the harvest index (HI), which may deviate from its potential value if significant water stresses are observed throughout the cropping season. For more specific information, Table  3  provides additional details, particularly regarding the soil and crop parameters involved. Within its inner hydro-agronomic module, Optirrig conducts a daily water balance considering factors such as infiltration, drainage, evaporation, transpiration, and change in soil water reserves (R1, R2, R3) in different reservoirs (surface, root zone, and deep) Optirrig relies on four daily climatic inputs: precipitation (P), mean air temperature (T) potential evapotranspiration (ETo), and total global solar radiation (Rg).
Sentinel-1 SAR Data In this study, a total of 288 S1 SAR images operating at the C-band frequency of 5. 405 GHz were utilized. These images were acquired by S1A and S1B satellites in both ascending mode (afternoon at 18:00 UT) and descending mode (morning at 06:00 UT). Specifically, 72 images (36 ascending and 36 descending) were obtained for each of the four reference plots (6-day revisit time) located in Montpellier (P1) and Tarbes (P2, P3, and P4). The data spanned from April to October for each year, with 2017 for P1 and 2018, 2019, and 2020 for P2, P3, and P4, respectively. This period corresponds to the irrigation period for these reference plots. The SAR images were acquired in the interferometric-wide (IW) swath with two polarizations: VV (vertical-vertical) and VH (vertical-horizontal).
For the purpose of this study, only the VV and VH polarizations were considered in the SSM (surface soil moisture) estimation algorithm. The 288 images were derived from the ground-range-detected (GRD) product, featuring a pixel spacing of 10 m × 10 m. These images were downloaded via the European Space Agency (ESA) website (https: //scihub. copernicus. eu/dhus/#/home, accessed on 9 November 2022). Figure  3  illustrates the repetitiveness of S1 data in both the ascending "A" and descending "D" acquisition modes across four study sites: Montpellier (P1) in June 2017 and Tarbes (P2) in June 2018. In Tarbes, S1 images from the same orbits as the P1 plot were acquired for P3 (2019) and P4 (2020). Each study site underwent the acquisition of 10 SAR images per month, comprising 5 ascending and 5 descending images.
The descending SAR image (morning) for the Montpellier site (Figure  3a ) was acquired 36 h before the ascending evening image, exhibiting incidence angles of 38. 1 • and 39. 3 • , respectively. Similarly, in Tarbes (Figure  3b ), the morning acquisition preceded the evening acquisition by 36 h, and the incidence angles were 36. 2 • and 40. 3 • , respectively. purpose of this study, only the VV and VH polarizations were considered in the SSM (surface soil moisture) estimation algorithm. The 288 images were derived from the ground-range-detected (GRD) product, featuring a pixel spacing of 10 m × 10 m. These images were downloaded via the European Space Agency (ESA) website (https://scihub. copernicus. eu/dhus/#/home, accessed on 9 November 2022). Figure  3  illustrates the repetitiveness of S1 data in both the ascending "A" and descending "D" acquisition modes across four study sites: Montpellier (P1) in June 2017 and Tarbes (P2) in June 2018.
To address this challenge, an effective strategy involves integrating SSM estimations from other SAR data sources. Collaborating with SAR satellites like ALOS PALSAR, RADARSAT, and COSMO-SkyMed can provide supplementary SSM data, effectively augmenting the frequency of observations. Additionally, leveraging the benefits of small-or micro-SAR satellites, which can be launched in constellations, offers a promising solution. These satellites provide increased revisit frequencies and higher spatial resolutions, which are essential for capturing frequent and detailed observations of irrigation patterns and soil moisture dynamics. By combining the data from different SAR sources, both large and small, a more comprehensive dataset covering a broader range of time intervals and spatial scales can be created.
Acknowledgments: The authors wish to thank the  French Space Study Center (CNES , TOSCA 2021) and the  National Research Institute for Agriculture, Food and Environment (INRAE). The authors wish to acknowledge the support and funding received from  Campus France  and  CNRS-L for M. Hamze 's PhD thesis. Furthermore, they extend their appreciation to the  European Space Agency (ESA)  for granting access to the Sentinel-1 data and to the  French Land Data Center (Theia)  for providing the Sentinel-2 data with atmospheric corrections.
The activity pattern of an animal describes variations in its activity over time. Longitudinal data on postures and standing activity were obtained from neural network image analysis of video recordings of 21 Large White and 22 Meishan lactating sows kept in individual pens. A clustering method was used to define groups of sows with a similar pattern of time budgets according to postures or standing activities, i. e. , the distribution of daily time spent in different postures or activities. Effects of sow behavior on piglet survival and growth were assessed using a survival model and a linear model, respectively. For each time budget, two groups of sows were identified before farrowing (D-3-D-1) and after farrowing (D1-D9), and three at D0. The risk of mortality was 23% higher if the sow spent more time eating and drinking while standing on D-1.
, 2009; Bilotta et al. , 2012; Zimmermann et al. , 2012). In particular, it allows us to identify correlations between complex hysteresis patterns and antecedent hydro-meteorological conditions  (Perks et al. , 2015). This method, however, is essentially a "black box" approach, and prediction often comes at the cost of understanding  (Vercruysse and Grabowski, 2019). Besides, it requires the acquisition of large datasets over multiple timescales, which restricts its application to a few wellinstrumented catchments. Despite this impressive body of work, modeling and predicting the entrainment, propagation, and deposition of suspended particles are still challenging tasks. Here, we use data acquired in the Capesterre river, a gravel-bed river that drains a small watershed on the southeast coast of Basse-Terre Island (Guadeloupe archipelago), to develop a phenomenological model of suspended sediment transport.
Based on a 4-year water-sampling campaign,  Lloret et al. (2013)  estimated that the mean flux of suspended matter is about 153 t km -2 yr -1. About 10 % of this flux consists of particulate organic carbon. La Digue's station is equipped with a pressure sensor (CS451, Campbell Scientific Inc. ) which measures the river stage relative to the fixed datum defined by a staff gauge installed by the Direction de l'Environnement, de l'Aménagement et du Logement (DEAL-Guadeloupe). In addition, a turbidimeter (OBS3+, Campbell Scientific Inc. ) measures the turbidity of the water. Both pressure and turbidity sensors are connected to a data logger (Campbell CR800), which has been recording their respective measurements every 5 min since 2013 (Fig. 1e  and f ). The station is also equipped with an automatic water sampler (6712 Full-Size Portable Sampler) triggered by a pressure probe (ISCO 720 Submerged Probe module).
Concentration of suspended material Sediment transport in the Capesterre river occurs only during floods. We therefore extract four flood events from our dataset and observe the evolution of the water level and of the concentration of suspended sediment in more detail (Figs. 2  and 3 ). All four floods exhibit the same simple shape: a steep rise followed by a slow decline. Visual inspection of the data reveals that this pattern is representative of the entire dataset, with multi-peak events representing less than a few percent of floods. During a flood, the concentration of suspended sediment follows the fluctuations of the water stage: it is equal to zero before the flood, rises when the water stage exceeds a threshold of about 20 cm, increases with the water stage until the flood peak, and finally decreases back to zero during the recession limb of the flood (Fig.
This happens when the size distribution of the suspended load evolves significantly during a flood. The ObsERA observatory used a LISST-StreamSide (Laser In Situ Scattering and Transmissometry, Sequoia Scientific Inc. ) to measure the size distribution of the particles in suspension in the Capesterre river during a flood in 2010. Their data reveal a bimodal distribution with two peaks (see Sect. 5. 1. 1). The first one, around 5 µm, corresponds to the wash load and remains remarkably stable during the flood. The position of the second peak varies between 23 and 32 µm. Assuming that this flood is representative, we expect no significant change of the turbidity-concentration relationship during a flood. Moreover, direct measurements of the concentration of suspended sediment, based on manual water sampling, confirm that the concentration versus waterlevel relation forms hysteretic loops  (Lloret, 2010; Lloret et al.
, 2013). Based on these observations, we therefore attribute the hysteresis of the turbidity of the Capesterre river to a hysteresis of its concentration of suspended sediment. In the Capesterre river, the flood peak is not systematically ahead of the concentration peak. Sometimes, it is the concentration peak that precedes the flood peak, and the concentration versus water-level relation then forms a clockwise hysteretic loop (Fig. 3 ). To determine the proportion of clockwise versus counterclockwise loops in the Capesterre river, we first extract individual flood events from our dataset. To do so, we define a flood as a period during which the water stage is higher than a threshold set to 20 cm. This value roughly corresponds to the threshold above which the flow is strong enough to carry suspended sediment.
For this purpose, we use the trust-region reflective algorithm method, implemented in the scipy. optimize. curve_fit function, and used in the model fitting wrapper of LMFIT (Non-Linear Least-Squares Minimization And Curve Fitting for Python) Python library  (Newville et al. , 2016). This optimization procedure is sensitive to the duration of the time period over which we apply it. In the next section, we discuss the calibration of our model on field data over a short period of time corresponding to a single flood event. The case of longer time periods, which encompass several flood events, will be discussed in Sect. 4. 3.
For the series of floods displayed in Fig. 5 , the rating curve systematically overestimates the concentration measured by the turbidity sensor. As a result, the sediment yield calculated from the rating curve exceeds the one measured with the turbidimeter by about 54 %. By comparison, the prediction of the model falls within 5 % of the data. These observations do not necessarily disqualify the use of the sediment rating curve. Indeed, the latter is constructed from samples collected by an automatic water sampler. The latter collects 24 samples, one every 15 to 60 min, as soon as the river stage exceeds a threshold set by the operator. In the dataset at our disposal, this threshold varies between 30 and 80 cm, which is way above the threshold of entrainment in the Capesterre river. As a result, most concentration data belong to the falling rather than the rising part of the hysteresis.
We are also grateful to Caroline Le Bouteiller for sharing her knowledge and expertise on the Laval catchment. Financial support. This research has been supported by the Labex UnivEarthS (grant nos. ANR-10-LABX-0023 and ANR-18-IDEX-0001). https://doi. org/10. 5194/esurf-11-363-2023 Earth Surf. Dynam. , 11, 363-381, 2023.
Code availability. The code is available at https://doi. org/10. 5281/zenodo. 7893930 (Roque-Bernard et al. , 2023). Data availability. Obsera data are available at https://in-situ. theia-land. fr/ (OZCAR, 2023) and DRAIX data are available at https://bdoh. irstea. fr/DRAIX/ (INRAE, 2023).
The samples were stored at À20 C until being analysed following the protocols described elsewhere  (Cawthray, 2003; Pang et al. , 2015). After the carboxylate extraction, roots were washed and separated from shoots. All nodules were removed from the root system. Shortly after harvest, roots with nodules removed were spread out on a transparent plastic tray with water to obtain root images using an Epson Perfection V700 scanner at 300 dpi. To obtain total root length, root surface area, root volume and mean root diameter, root images were analysed using WinRhizo 2009 software (Regent Instructions). Root samples after scanning, nodules, and shoot samples were dried at 70 C for 72 h and then weighed separately for DW. Root mass ratio was calculated as the ratio of root DW (excluding nodules) to total plant DW.
ACKNOWLEDGEMENTS The work was funded by the  Australian Research Council  ( LP200100341 ) and the  UWA Institute of Agriculture. We thank  Mr Rob Creasy  and  Mr Bill Piasini  for helping to maintain the plants in the glasshouse,  Drs Jun Zhou ,  Hongtao Zhong ,  Daihua Ye ,  Feng Wang  and  Jingwen Gao  for help with the glasshouse harvest,  Mr Greg Cawthray  for help with the analysis of carboxylates. Open access publishing facilitated by The  University of Western Australia , as part of the  Wiley -The University of Western Australia agreement via the Council of Australian University Librarians.
The field of view was of 192 × 192 mm, the matrix size was 64 × 64, and the total EPI imaging time was 20 min 25 (490 volumes × 2. 5 s/volume, 4 initial volumes as dummy scans). The first two acquired volumes were excluded from the data analysis, meaning that no stimulation was performed during this period. Imaging was performed on 24 animals. Three animals were excluded as we detected a loss of MR signal in the frontal lobe due to the anatomical presence of an air cavity anterior to the brain and 1 animal was excluded because of movements during the anatomical image acquisition inducing poor realignment and segmentation.
Acknowledgments We want to thank all the staff from the  Rennes pig experimental facilities  ( UE3P ), especially  Eloïse Delamaire  as the head of the facilities, as well as  Josselin Delamarre  and  Marie-Hélène Lohat  for taking care of the animals. We also thank the EAT team and especially  Sylvie Guérin  for the ad hoc help. Part of this work has been performed using the PRISM core facility (Biogenouest,  France Life Imaging ,  Univ Rennes ,  Univ Angers ,  INRAE ,  CNRS, France). We especially acknowledge  Stéphane Quellec  for his technical support. We also want to thank  Sylvie Carayol  and  Christophe Bousquet from Phodé  for their work for the conception and provision of the functional ingredient.
Metagenomic assembly and binning We performed metagenomic analyses using anvi'o v7  [43, 44]  and the metagenomic snakemake  [45]  workflow from the quality filtering to the merging of profile databases generated for each organ separately using the "anvi-run-workflow" program and the "-workflow metagenomics" flag. All the parameters used to set the snakemake workflows are written in the 'config. json' files available in the Data Availability section. Although the ovaries raw reads have already been analyzed in  [42] , we herein reanalyzed both ovaries and midguts raw reads following an exactly similar protocol in order to generate consistent analyses and comparable results for both organs. Briefly, during each workflow, we quality-filtered the raw reads from each sample using illumina-utils  [46]  v2.
10 and the "iu-filterquality-minoche" anvi'o program with default parameters. We assembled quality-filtered reads into contigs using MEGAHIT  [47]  v1. 2. 9, keeping only contigs with a length > 1000 nt. We performed read recruitment analyses by mapping the quality-filtered reads from all ovary samples onto the contigs of each ovary sample with "all against all" flag in config files using Bowtie2  [48]  v2. 3. 5. 1 and repeated the same procedure for midgut samples. We then used the "anvi-gen-contigs-database" program to generate anvi'o contigs databases for each individual assembly. This program computed k-mer frequencies for each contig, soft-split contigs with length > 20,000 pb into smaller ones and identified ORFs in each contig using Prodigal  [49]  v2. 6. 3. We used the "anvi-run-hmms" program to identify HMM hits searching against the HMM sources included by default in anvi'o (Bacteria_71, Archaea_76 and Protista_83) and the "anvi-run-ncbi-cogs" program to assign functions to genes which search gene amino acid sequences against the COG20  [50]  database using blastp  [51]  v2.