dataesr/scientific-paragraphs-categorization huggingface dataset:
Approach based on S1 time series Over a period of two years, the S1 time series are analyzed  (Gao et al. , 2018). The mean of the radar signals, its variance and the temporal correlation length are estimated from the radar time series. These indicators are used to separate irrigated and nonirrigated classes. Three classes are identified. The results show a very good accuracy of 81% using the SVM approach (Figure  3 ). The average radar signal is the best indicator for separating irrigated areas from non-irrigated areas. Variance and correlation length are considered to separate irrigated trees and irrigated annual crops. Validations of the classification are based on the SIGPAC database over 26,434 fields in the whole study area. The use of Random forest approach gives the same results as for SVM.
ACKNOWLEDGEMENTS This research was supported by the  French Space Study Center (CNES ,  DAR 2018 TOSCA ). The authors wish to thank the  European Commission  and the  European Space Agency (ESA)  for kindly providing the Sentinel-1 images. We used Copernicus Sentinel-2 data processed to level 2A by the  French  land data service center (Theia: https://www. theia-land. fr). The authors wish to thank technical teams of CESBIO, IRSTEA,  IRD  and  INAT  for ground measurements.
Their transportation from regional storehouse to the farm was not included due to lack of data. The transportation of FB by truck was included. The manufacturing and transportation of small materials and machines such as chemical sprayers, basins, wheelbarrow, watering cans and pumps were excluded due to their very small expected contribution. Only for the large-farm surveyed (see next section), agricultural machinery was included for land preparation by using a complete process available in the ecoinvent 3. 3 database  [5].
Transportation stages Based on discussions with surveyed companies and farms, we concluded that transportation distances by road in Kenya could vary a lot across situations but no data on the average distances of transport of FB was available. Based on expertise from our local expert, we defined a baseline scenario of transportation in Kenya by a lorry of 3. 5 to 7. 5 metric tons capacity over 50 km before packhouse and by a refrigerated lorry of same capacity over 50 km after packhouse. Regarding the transportation by air-freight from Nairobi airport to London airport, we calculated a distance of 6 750 km using the calculator available at: http://www. worldatlas. com/travelaids/flight_distance.
Field emissions and fluxes We used emission factors from IPCC  [8]  to estimate direct (1% of nitrogen inputs) and indirect (1% of NH3 emitted and 0. 75% of NO3 emitted) nitrous oxide emissions (N2O) and to estimate nitrate (NO3) leaching (as 30% of nitrogen inputs). Despite the lack of specificity of its emission factors, the IPCC report remains the most easily applicable to estimate emissions in the study's context, since the IPCC database includes measurements in tropical conditions. For ammonia (NH3) emissions from mineral fertilizers, emission factors from Bouwman and Van Der Hoek  [9]  were used since they correspond to tropical conditions (4% for NPK, 2% for Calcium Ammonium Nitrate and Di-Ammonium Phosphate). We based our estimation of NOx emissions on Nemecek and Schnetzer  [10]  assuming a ratio of 0.
[13]. Field water fluxes were generally unknown by the SHF themselves. It was not possible to do a proper water balance to estimate the water actually consumed. The amount of water withdrawn was estimated based on the expertise of our local expert taking into account the rainfall levels in the different counties. An amount of 400 mm/y was assumed for all plots from Machakos, Meru, Murang'a and Kirinyaga counties while an amount of 150 mm/y was assumed for all plots from the Trans Nzoia County where rainfalls are more abundant. Overall, the water is generally transported to the farm at no energy and financial costs. For all counties except Trans Nzoia, where no data was available for fuel use, only six farmers had declared fuel consumption for water pumps. This was quite consistent with the expertise of our local expert, of about 10% of farmers needing a gasoline pump for water.
Background processes Background data for energy production [14] , fertilizer production [11] and pesticide production [15]  were mostly based on processes from the ecoinvent database (Ecoinvent 3 Allocation, recycled content, Unit) and the Agri-footprint database with economic allocation (Blonk Agri-footprint BV), available in the SIMAPRO software (version 8. 3. 0. 0). The transportation stages from the ecoinvent processes for energy materials and inputs were not adapted to the Kenyan situation since this was not expected to have an important effect on the results. For developing the inventory of multi-nutrient fertilizers, which are used extensively in French bean crops, we applied the method from Nemecek and Kägi  [11].
Only the diafenthiuron could be characterized according to the substance group's factor (thiourea). For all other active ingredients with no characterization factor (CF) in ReCiPe, we calculated and tested the max and the mean CF for all pesticides used in our dataset. Results calculated with the mean CF were retained, since both calculations gave close results. For evaluating the impact of water consumption, we used the method from Pfister et al. [16] , which proposes CF compatible with the Endpoint version of ReCiPe.
Data Quality All farms and packhouses surveyed were part of the studied population. A weighted average was calculated for the SHF-contracted type to account for the contribution of the different counties to the total production of fresh FB for this major type. However, the sample size was quite small compared to the total population and its inherent variability and representativeness cannot be claimed for our results. Primary data were collected for all inputs and outputs of farm and packhouse stages but data gaps remained, especially for energy and water use for irrigation, which were filled thanks to the expertise of our local expert Andrew Edewa. The main gaps and uncertainties of our dataset are as follows:  potential mistakes on primary data given the lack of formal records of farmers,  the uncertainty on farm inputs especially regarding compost rates, water and energy use for irrigation,  the uncertainty due to the use of default emission factors for estimating field emissions and the noninclusion of N fixation, and  the uncertainty attached to the losses of FB across the supply chains.
The data quality of our dataset was assessed globally based on recommendations from the ILCD handbook  [17]. This data quality assessment is based on six data quality indicators, namely: technological representativeness (TeR), geographical representativeness (GeR), time-related representativeness (TrR), completeness (C), precision and uncertainty (P), and methodological appropriateness and consistency (M). For each indicator a score between 1 and 5, 1 being the best score and 5 the worst, is given independently. Then, the overall quality of the dataset can be derived from the quality rating of the various quality indicators based on Eq. (  1 ):.
𝐷𝑄𝑅 = Eq. (  1 ) with Xw the weakest quality level obtained among the data quality indicators and i the number of indicators scored. Values given for the different data quality criteria were as follows: TeR: 1; Ger: 2; TrR: 1; C: 2; P: 3; M: 2, resulting in an overall value of DQR calculated for our datasets of 2. 3, corresponding to a basic quality (between 1. 6 and 3).
CONCLUSION A complete LCA study including the diversity of farm practices and all key impacts (water deprivation, (eco)-toxicity) was performed using Endpoint indicators for decision-makers on the fresh FB value chain of Kenya. It allowed for identifying hot-spots at both market and farm-gate where efforts should be made. Drastic reduction of impacts could potentially be achieved by designing stabilized FB products that could be sea-freighted. At farm level, training of farmers and better recording of practices could also help improve the eco-efficiency of farms. However, given the sample size and the uncertainty attached to certain data and results, our study should be seen as a preliminary study needing validation through the analysis of a greater sample of stakeholders but also a more in-depth analysis of impacts due to pesticides applications.
ACKNOWLEDGEMENT The authors are grateful to all farmers and stakeholders in Kenya who provided their data and kindly offered their time for this study. Many thanks are also expressed to Dr. Tommie Ponsioen who reviewed this study and to all the DEVCO team following the study for their constant support and help. Authors are grateful to the rest of the expert team: Ulrich Kleih and Catherine Allen for their efficient and friendly collaboration during the study. This LCA study was fully funded by DG DEVCO [VCA4D, 2016/375-804]. Finally, the authors are grateful to the three anonymous reviewers who contributed to improve this manuscript.
Acknowledgments Hardouin's research was conducted as part of the project  Labex MME-DII  ( ANR11-LBX-0023-01 ). Cressie's research was supported by the  Australian Research Council Discovery  Project no. DP150104576 , and by the  US National Science Foundation  under  NSF  grant  SES-1132031  funded through the  NSF-Census Research Network (NCRN) program.
In this study, we analyse an extensive dataset based on GPS locations of 237 male and female red deer in Norway. Earlier works on migration behaviour of red deer in Norway have shown that not all individuals are migrating, and that migratory individuals gain access to newly emergent plants and thus to higher quality diet by migrating between separate ranges compared to remaining in their winter ranges  11. Plant phenology drives spring migratory behaviour in red deer, while onset of hunting is an important trigger of autumn migration  11, 24. In such context, we investigate how different seasonal and sex-specific limitations affected the variation in a suite of migration characteristics at the scale of the migration event (timing of departure, total distance roamed, duration of migration, movement speed, use of stopover sites) and using data on daily activity patterns.
001; Fisher ratio of variance = 6. 03 [5. 97 6. 09]; Fig. 3 ). Data analysis. Some individuals were removed due to missing values in the covariates, 237 individuals were then available for analyses on migration characteristics (including 309 spring migration events and 241 autumn migrations events) and 147 on daily activity pattern (including 185 spring migration events and 147 autumn migrations events). Variance analyses. To test if the variances of the 5 migration characteristics (timing of departure, total distance roamed during migration, duration, mean travel speed and number of stopovers used during migration) differ between seasons (spring vs. autumn) and sexes we used Levene's tests implemented in the R package 'car'  67. tests allow us to test our predictions that migration timing will be less variable for males than females if linked to our hypothesis that rut determines male autumn movements, and that migration departure will be more synchronised if linked to onset of spring growth, while autumn migration is expected to be less well synchronised as it may be unrelated to forage conditions.
Variables were log-transformed to optimize normality (see Supplementary material S2 for distributions), but since the normality conditions were not always fully met, the more robust Levene's test was used instead of the more commonly used Fisher test. Model structure -migration characteristics. To answer our question on seasonal and sex-specific variation in migration characteristics, we used linear or generalized linear mixed models implemented in the R package 'lme4'  68. Specifically, we modelled the timing of departure (defined as the individual Julian date of departurethe median Julian date of departure of the corresponding season), the total distance roamed during migration, the duration, mean travel speed and the use of stopovers (binary variable) according to the season (spring vs.
autumn), sex, county, distance of the winter home range barycenter to coastline, total distance roamed during migration, timing of departure and duration. Patterns were also expected to be sex-and season-specific, so two-way interactions with sex and season were considered for all variables (except for the interactions between duration and season and total distance roamed and season to avoid over-parametrization of the full model). Individual identity and year were included as random intercepts in all models to account for unbalanced sample size. Since the type of activity data differed depending on collar brand this factor was included in the models explaining the proportion of daily time active. All variables were rescaled and/or transformed when necessary to optimize estimation  69. The total distance roamed during migration, the duration and mean speed were log-transformed, the absolute difference in elevation during migration, the distance between seasonal ranges and departure timing were rescaled by centring on the mean and dividing by the standard deviation, and the proportion of daily time active was transformed using an arcsin (x*2/π) function allowing the results to be rescaled between 0 and 1 (see Supplementary material S2 for distributions).
Model selection. To avoid correlation issues we checked that all variables included in the same model were not correlated more than a conservative threshold of r = 0. 3  69. As the total distance roamed and duration of migration were highly correlated (Pearson's product-moment correlation: correlation = 0. 83, n = 5665, t = 113. 26, df = 5663, P < 0. 001), these factors were not included in the same model. We fitted the global models described above as well as all simpler derived models in R using the AICcmodavg package  70. The best models were then selected using the Akaike Information Criterion corrected for small sample size (AIC c ), which reflects the best compromise between model precision and accuracy  71. According to the rule of parsimony, we selected the simplest model within 2 AICc of the top model.
Sioux Falls Penn. Sate Univ The stations experience different climates and different ground properties. The Fort Peck station lies in a flat agricultural area with grasses and few trees, like the Bondville one. Fort Peck experiences a high interannual variation in snow cover. The Sioux Falls station is situated on herbaceous grounds near the Earth Resources Observation and Science (EROS) Data Center. The Penn. State Univ. station lies on an agricultural research farm in a wide Appalachian valley and is surrounded by grass with crop in the southwest quarter. Similarly, the Goodwin Creek station is situated in a rural pasture. The surface surrounding the Table Mountain station is sandy with a mix of exposed rocks, small cacti, desert shrubs, and sparse grasses. The flora is usually green in the late spring and early summer, and browns significantly by midsummer.
The Desert Rock station is also in a desert-type landscape and  The stations experience different climates and different ground properties. The Fort Peck station lies in a flat agricultural area with grasses and few trees, like the Bondville one. Fort Peck experiences a high interannual variation in snow cover. The Sioux Falls station is situated on herbaceous grounds near the Earth Resources Observation and Science (EROS) Data Center. The Penn. State Univ. station lies on an agricultural research farm in a wide Appalachian valley and is surrounded by grass with crop in the southwest quarter. Similarly, the Goodwin Creek station is situated in a rural pasture. The surface surrounding the Table Mountain station is sandy with a mix of exposed rocks, small cacti, desert shrubs, and sparse grasses.
Inputs to Libradtran In cloudless atmospheres, the PAR depends mostly on the solar zenith angle θ s , the TOC and TWV, the aerosol optical depth (AOD) and type, the vertical profiles of the temperature, pressure, density, and volume mixing ratio for gases, the elevation of the site above the mean sea level, and the ground albedo. The origins of the inputs are selected, taking into account that the method will be used in an operational mode to provide estimates of the PAR-irradiance and PPFD-at any location and any time. θ s is given by the SG2 algorithm  [35]. The TOC and TWV, and the aerosol optical depths for organic matter, black carbon, dust, sea salt, and sulfate, originate from CAMS. The five vertical profiles are: tropics (coded afglt), mid-latitude summer and winter (afglmls and afglmlw), and sub-Arctic summer and winter (afglss and afglsw), from the Air Force Geophysics Laboratory (AFGL) data sets.
A map of weights by Gschwind et al. [36]  indicates which ones to use at any location, as well as their respective weights. The digital terrain model is the so-called SRTM data set that derives from the Shuttle Radar Topography Mission. When no information on the type of surface and on the ground albedo in the PAR is available, we have adopted the approach of Bosch et al. [22] , where the PAR albedo is equal to 0. 47 times the broadband albedo. The albedo is defined as the ratio of the global upwelling irradiance to the global downwelling irradiance in a given spectral band. It is also defined as the integral of the bidirectional reflectance distribution function (BRDF), depending on the surface-type and its roughness. Here, the broadband albedo is given by the series of maps of Blanc et al.
Data Availability: PAR measurements at each station were provided by the SURFRAD network established in 1993 through the support of the NOAA Office of Global Programs. Measurements used here are freely available and were downloaded from ftp://aftp. cmdl. noaa. gov/data/radiation/surfrad/, last access: 14-04-2019. Products from CAMS are freely available after registration and were downloaded from: http://atmosphere. copernicus. eu/, last access: 14-04-2019. The McClear products are freely available after registration and were downloaded from: http://www. soda-pro. com, last access: 04-14-2019. The BRDF maps by Blanc et al. [31]  may be downloaded from: http://www. oie. mines-paristech. fr/Valorisation/Outils/AlbedoSol/, last access: 14-04-2019.
At 95 days post fertilization, we measured egg diameter of a sample of 25 eggs per size class and a random sample of 50 eggs. This was done to validate our sorting into small and large egg size classes and compare those to the egg-size distribution in the population. The eggs were photographed and the diameter of each egg was later measured from these photographs using ImageJ software  [53, 54]. Egg sizes ranged from 31 to 50 mm representing egg sizes of Arctic charr from the Holar breeding program  ([53] ;  Fig 1). Small (S) eggs ranged from 30 to 37 mm (mean ± SD: 34. 28 ± 1. 70 mm) and differed from large (L) eggs ranging from 45 to 50 mm (mean ± SD: 47. 16 ± 1. 28 mm; t-test: t = 30. 30, p-value < 0. 0001). These selected eggs were representative of the lower and upper quarter of the size range (Fig  1).
Social treatment We tested the effect of social environment by observing one fish maintained in a group of six fish versus fish put in isolation for the purpose of the observation (brief isolation; Table  1 ). After 24 hours, the observed individual in the group of six fish was chosen as the first fish crossing a randomly chosen area of the compartment a few seconds before the observation. Naive fish were used each week (age 63, 70, 77 and 84 dph) for fish in groups or briefly isolated. After each week, focal fish were raised in a different compartment and were never observed a second time. However, fish that were fully isolated (i. e. long-term isolated) were the same individuals. They were screened repeatedly each week for behaviour and this data set was analysed separately from the fish in groups or fish briefly isolated (see below).
e. fish were measured every week). Hierarchical variance between the models was checked by running all possible order of factors of this 3-way ANOVA, which return essentially same results (see Results section). Growth of first-feeding fish was only assessed in fish kept in groups. We did not expect growth differences between fish isolated for the time of observations (isolated for only two days) and the fish raised in groups. Behaviour variables. In this study, two types of behavioural data were analyzed using two-part models, one on fish foraging (bottom, water column, surface and total foraging) and another on fish activity (proportion of time spent immobile). All foraging variables were analysed separately, but "total foraging" represented the total number of all foraging events/ attempts observed i.
Finally, the effects of the independent variables were estimated from the reduced models and their significance was tested by likelihood ratio tests (LRT) between nested models respecting marginality of the effects that are supposed to follow a χ 2 distribution under the null hypothesis (type II tests;  [57] ). All foraging behaviour variables (bottom, water column, surface and total foraging) were analyzed using Hurdle Negative Binomial models (HNB) as the data collected here were count data with excess zeros. These models are called two-part models: the first part is a binomial probability model that governs the binary outcome of whether a count variable has a zero or a positive value and the second part is a zero-truncated count model that governs the positive outcomes  [58]. In this study, this type of model allowed us to test the effects of the independent variables cited above on (i) the probability of fish that forage/move versus the ones that do not, and (ii) to test the effects of the same factors only among the fish that were mobile or foraged.
Fish activity was analysed using two-part GLM models. The first part is a binomial probability model that governs the binary outcome of whether the activity of fish has a zero or a positive value (i. e. mobile or immobile), and the second part is a normal distribution on the positive outcomes (only considering the fish that showed immobility). Percentage of time spent immobile (i. e. fish activity) was log transformed. The independent variables in the full model were similar to the one for the foraging variables. As described above, fish observed in groups or briefly isolated were analysed together i. e. naive fish were used at each age (Table  1 ). Observations of long-term isolated fish were repeated data on similar individuals over time and therefore were analyzed separately from the two other social categories (Table  1 ).
These observations were analyzed using generalized linear mixed-effects models (GLMM). A random effect that affected the model intercept only was used to account for variability due to individuals. In this case, the full model was reduced by backward selection in two steps: the random part of the model was reduced first and the fixed part was selected afterwards  [56, 59]  Selection was based on significance of the effects at a 5% alpha risk threshold determined by likelihood ratio tests (LRT) between nested models while respecting the marginality of the effects. The GLMM models were fitted using the package lme4 in R  [60]. The full model included individual as a random effect and rearing temperature (4. 5 ˚C vs. 6. 5 ˚C), age of fish (63, 70, 77 and 84 dph), egg size group (small vs. large), and day of observation (two consecutive days; day1 vs.