dataesr/scientific-paragraphs-categorization huggingface dataset:
The inventory covered farm operations, as well as the following processes: production of farm inputs such as energy carriers, fertilizers, juvenile, exogenous feeds (including ingredient production and processing), and farm equipment and infrastructures (i. e. , equipment; construction of buildings and breeding infrastructures) and transportation at all stages. Water flow at the outlet of each pond was measured daily to estimate the water balance. The fate of nitrogen (N) and phosphorus (P) was based on a nutrient-balance experiment performed on-site involving chemical analysis of fish, fertilizers, feeds, inlet and outlet water, water and sediment  (Pouil et al. , 2019). Data for pond gaseous emissions were adapted from the literature. Gaseous fluxes are influenced significantly by factors such as drainage, primary productivity, and the availability of organic matter in aquaculture ponds  (Yang et al.
Background data (i. e. , construction materials, fuel, rearing equipment) were based on the ecoinvent v3. 8 database  (Wernet et al. , 2016)  and adapted to the local context. The inventory data for Indonesian electricity production were obtained from EIA (2021). Data on feed production were collected based on detailed reports supplied by the feed manufacturer in which all feed ingredients were quantified while proximate analysis were performed to determine nutritional composition (Table  2 ). The feed formulations were then validated regarding nutritional constraints according to  Guillaume et al. (1999). Data on feed processing were supplemented with detailed information from a local feed producer. The inventory data for local fishmeal production (discarded fish) was collected from a local fishery processing plant dedicated to frozen fish (for export), surimi and fishmeal (Palembang, Sumatra Island).
Heat energy from wood wastes was used for fishmeal processing  (Mungkung et al. , 2013). Inventories for crop-based feedstuffs were adapted from the ECOALIM dataset  (Wilfart et al. , 2016)  to consider transportation to Indonesia. Distances for road and boat transport were estimated using Google Maps®. For organic fertilizer, we used the inventory for poultry manure on the global market available in the ecoinvent v3. 8 database while inventory of rice produced in China, from the same database, was used for rice bran considering a mass allocation  (Ayer et al. , 2007).
(2004) ; • water dependence (m 3 ) included the volume of water used in ponds as proposed for aquaculture production systems by  Aubin et al. (2009)  and the amount of water used in the other production phases. The impact assessment was conducted according to (1) the International Reference Life Cycle Data System (ILCD) method for acidification and climate change, (2) the CML IA method, version 3. 06 for eutrophication potential, and land occupation. Total energy use was calculated using the Cumulative Energy Demand method, version 1. 10. Calculations were made using SimaPro software 8. 5. 4. 0 (PréConsultants, Amersfoort, The Netherlands). As mentioned earlier, the ecoinvent version 3. 8 database was used for background data, and the Agribalyse® database  (Koch and Salou, 2014)  was used for agricultural machinery, agricultural products and feed ingredients (ECOA-LIM dataset;  Wilfart et al.
, 2021) , made it possible to estimate uncertainties related to the exogenous feed gain in the implementation of such a production system. The uncertainties related to Azolla production mainly affect the impact categories in which exogenous feeds contribute strongly. Therefore, unsurprisingly, we observed higher variability in the CoC systems for NPPU, climate change, CED and, to a lesser extent, land occupation. In addition to Azolla production, the other sources of uncertainties for all impact categories include variability among the two replicates of each system, the inherent variability already accounted for in the ecoinvent database and the assumptions made for mass-balance modelling. Interestingly, most of the data we recorded from the replicates of the two systems were, overall, comparable.
There was a relatively high uncertainty on water dependence, particularly for the MoC system. Indeed, the ponds used, connected in series and fed by the same river, experienced fluctuating flows during the production cycle. Although this only had a minor effect on fish production, the water inputs in the study were highly variable, especially for MoC. Nutrients (N and P) fluxes can be an important source of uncertainties in LCA aquaculture studies. Here, most of the data used were extracted from chemical analysis of each compartment included in the production systems (i. e. water, feeds, fertilizer and fish), and the N and P quantities stored in sediments were taken from  Pouil et al. (2019)  that gives nutrient budgets in the same ponds with an MoC system of giant gourami. Assuming the occurrence of Azolla in the CoC was not drastically affected by particulate N and P in sediments (related to uneaten feed, faeces, plankton sedimentation, dead fish, etc.
Acknowledgements This work was financially supported by the  French National Research Agency  ( ANR-RF-2015-01 ) through the  IMTA Effect project  of the  ERA-NET COFASP program. The authors thank  Bang Macho  and the local fish farmers in the village of Babakan for sharing their experiences and knowledge. The authors acknowledge  Ahmad Sihabuddin ,  Sumanta  and  Gusnia Sundari  from the  IRD  for their help in data collection. This is the publication ISEM 2023-217 SUD.
Vervet monkeys are a well-suited species to investigate social traditions because they live in multi-male, multi-female groups, providing individuals with many opportunities to socially learn from different individuals. A body of studies has shown that vervet monkeys socially learn how to manipulate artificial fruits  [9, [48] [49] [50] [51]  and to process novel food  [52] , but their potential to socially learn social behaviours remains unknown. Here, we focused on two main aspects of primate social life. First, sociality (i. e. , propensity to be affiliative) and second, grooming reciprocity (i. e. , how reciprocal are grooming exchanges). We asked three main questions: (I) Is sociality influenced by group identity across years? If yes, do dispersing males adapt their sociality when they integrate a new group? (II) Is sociality similarly distributed between individuals in each group according to their socio-demographic category and kinship? (III) Are differences in grooming reciprocity between groups coherent with differences in sociality? Here, we extend previous studies by gathering three grounding aspects of research on animal culture: the study of several neighbouring groups with overlapping home-ranges and migration flux; the quantification of the influence of group demographics, and the use of a continuous long term database.
On the basis of established ethograms of social behaviours (S1 Table, S2 Table, S3 Table ), we defined affiliative interactions as interactions including only affiliative and neutral behaviours and agonistic interactions as interactions including only agonistic and neutral behaviours. Our dataset included a total of 68 443 affiliative interactions and 16 259 agonistic interactions. In 2017, three months (June, July, August) of missing time of observation and/or affiliative interactions were excluded from our analysis. Observers recorded behaviours as point events. To assess sampling effort, we took the total amount of time spent in the field during field days as a proxy for the effort of ad libitum sampling in each group. We assessed this effort by computing the duration of observation of each group per day which we multiplied by an attenuating factor of the number of observers for each field session according to a binary logarithmic function, so that the weight of each observer decreased as the number of total observers increased (AK mean ± SD: 2706 ± 803, BD mean ± SD: 3360 ± 614, NH mean ± SD: 3439 ± 974).
Acknowledgments We thank the onsite managers,  Albert Driescher  and  Arend van Blerk  and all the field assistants, Master students, PhD students and postdocs who collected the data over the study period. We are grateful to the van  der Walt  family for giving us the permission to conduct the study on their land. We thank  Pooja Dongre  for her advice on the Elo-rating calculation and  Rachel Harrison  and  Frédéric Schütz  for their statistical advice. This project was funded by the  Swiss National Science Foundation  ( P 300P 3 1 51187 ,  31003A 1 59587 ,  P P 00P 3 1 70624  and  P P 00P 3 1 98913 ) along with  Branco Weiss Fellowship-Society in Science  (granted to EvdW) and to a  Fyssen grant  (granted to CC).
As input for the models, reference soil group and textural properties, geolocation, N and C contents, terrain elevation and slope, soil pH and mean annual precipitation and temperature from 108 sites of the RAINFOR network were used. The selected models were then applied to predict the target P forms using several spatially explicit datasets containing contiguous estimated values across the area of interest. Here, we present a set of maps depicting the distribution of total, available, organic, and inorganic P forms in the topsoil profile (0 -30 cm) of the pan-Amazon region in the spatial resolution of 0. 5 x 0. 5 degrees. The random forest regression models presented a good level of mean accuracy for the total, available, organic, and inorganic P forms (77. 37 %, 76,86 %, 75. 14 %, and 68.
These maps are built upon models of varying complexity that link soil P pools to variables like soil type, soil age, lithology, soil C and N content and soil texture (e. g. ,  Castanho et al. , 2013; Goll et al. , 2012; Wang et al. , 2010). The global maps of soil P forms created and described by  Yang et al. (2013)  (available in  Yang et al. , 2014)  were derived from several global soil datasets combined with the current scientific understanding of P transformations during pedogenesis. In this approach, total P content was obtained from estimates of initial rock P content and model-based P loss rates from soil chronosequences. The attribution of each P fraction, from the estimated total P, is based on averaged values of each fraction extracted sequentially in different soil reference groups  (Yang et al.
Phosphorus data: the fitting dataset The soil and other environmental measurements at 92 sites selected  (Hou et al. , 2018)  for this study originally presented in the studies of  Quesada et al. (2010)  and  Lloyd et al. (2015). The soil P measurements and analysis are described and standardized by  Quesada et al. (2010)  and refer to the 0-30 cm depth of the soil profile. Additionally, we included here sixteen unpublished site data from the study of  Quesada et al. (2020) , resulting in a total of 108 observations (Fig. 1  and S6 ). All soil samples were collected under the RAINFOR protocol for soil sampling  (RAINFOR, 2022). The soil samples were submitted to a sequential P extraction by Hedley fractionation procedure  (Carter and Gregorich, 2008; Hedley and Stewart, 1982; Tiessen and Moir, 1993).
In this dataset, the P fractionation data is complemented by several variables. For this study, soil texture (sand, silt, and clay fraction), C and N content, pH, elevation, soil reference group, mean annual precipitation and temperature, latitude, and longitude were used (Table  1 ). The calculations of soil attributes, like pH, texture, and C and N contents are described in  Quesada et al. (2010). Elevation and climatic variables were extracted by  Quesada et al. (2010 Quesada et al. ( , 2020) )  and  Lloyd et al. (2015)  from a digital elevation model (Shuttle Radar Topography Mission database -Digital Elevation Model -SRTM-DEM) and the WordClim dataset  (Fick and Hijmans, 2017) , respectively. The slope for all sites was calculated using the same digital elevation model  (Saatchi, 2013)  with 30 arc-seconds of resolution.
Predictive variables for constructing the P maps: the predictive dataset The predictor data is composed of a set of five distinct spatially explicit datasets in raster and vector formats. Spatial and numerical transformations were applied to the original datasets to fit the purpose of this study. The SOTERLAC (Soil and Terrain database for the Latin America and Caribe) vector data of soil types  (Dijkshoorn et al. , 2005)  for the study area were rasterized, respecting the original scale of 1: 5. 000. 000, to 30 arc-seconds of lat-lon resolution. The major WRB (World Reference Base for soil resources) reference soil groups were attributed following  Quesada et al. (2010). Mean annual precipitation and temperature (MAP, °C and MAT, mm/year), were extracted from the WorldClim database  (Fick and   (Wieder, 2014)  for the topsoil (0-30 cm).
Soil total N (TN, %) for the topsoil, was extracted from the -IGBP-DIS dataset  (Global Soil Data Task, 2014). The elevation data was extracted from the SRTM-DEM of the Latin America  (Saatchi, 2013 , Farr et al. 2007 ). Terrain slope (%) was estimated from the elevation data. All spatial raster data sets were aggregated to the 0. 5° x 0. 5° resolution. The reference soil group map was aggregated to the final resolution using the modal value, and the remaining datasets were aggregated using the mean values. The categorical variable reference soil group was transformed into a set of binary variables using one-hot encoding before the application of the trained random forest models to the prediction of the phosphorus maps. Less common reference soil groups found in the region were excluded during the aggregation of the raster datasets (Table  S4 ).
Leptosols, a common soil reference group in the elevated areas in the Andes and the northern and southern crystalline shields, were not sampled, i. e. , were not in the fitting dataset. These were considered undefined and received the value zero or false to all sixteen binary variables representing the reference soil group in the predictive dataset (Table  S4 ). To identify the areasi. e. , grid cells, where the predictor data presents high multivariate dissimilarity with the observed data, such that the predictive power of the random forest models can be compromised, we calculated the Dissimilarity Index (DI,  Meyer and Pebesma, 2021). The DI denotes the multidimensional dissimilarity of a given predictor grid cell (a spatial grain composed of the data described in this section) in relation to the fitting dataset (the phosphorus dataset, described in the previous section).
Random Forest models We employed a model selection approach based on model regressions using the random forest algorithm  (Breiman, 2001; Cutler et al. , 2012). For each target P form (available, organic, inorganic, and total P), 10 5 random forest regression models were trained and tested using the fitting dataset (Sect. 2. 1). We used the Scikit-Learn RandomForestRegressor  (Pedregosa et al. , 2011). For each random forest model being fitted, 100 decision tree estimators were used. The phosphorus dataset was randomly split into training and test data (75% and 25% of the samples, respectively). During the training and testing, every fitted model and its relative dataset split were assigned to a random state, represented by an integer between 0 and 99999. Thus, we were able to identify each model and associate it with the division of the phosphorus dataset for training and testing.
As criterions for the selection of random forest models, we adopted an accuracy measure (Eq. 1) based on the Mean Absolute Percentage Error  (MAPE, De Myttenaere et al. , 2016)  and a Monte Carlo cross-validation procedure  (Stone, 1974; Kuhn and Johnson, 2013)  in which the models were cross-validated on fifteen random splits of the phosphorus dataset, using the same ratio of the fitting dataset splitting as in the training phase. The metric used to evaluate the model's performance in the crossvalidation phase was the coefficient of determination (R 2 ). For each target variablei. e. , P form, we selected the models with accuracy and cross-validation R 2 scores above arbitrarily chosen threshold values based on preliminary evaluations of a thousand random forest regression models for each target variable.
The MDA is calculated via permutation where a given selected model is tested several times (120 in our case) with rearrangements (shuffling of a single feature) in the model's respective testing split of the fitting dataset. These rearrangements aim to eliminate the potential relationships between the permutated features and the target variable and estimate how much accuracy is lost in the process. Thus, for a given model, features that show higher values of MDA provide higher predictive power. To identify the effects of the most important input features on the predictions of the random forest models, we calculated the partial dependence  (Hastie et al. , 2001)  and plotted the individual conditional expectation  (Goldstein et al. , 2014)  for the most accurate random forest model selected for each P form.
To generate the final maps of the P forms we grouped the maps predicted by the selected models for each P form via mean and calculate the standard error (both in mg kg -1 ). We used the topsoil bulk density aggregated from the HWSD-v1. 2 dataset to calculate the stocks of each P form in the topsoil (in Pgpetagrams). The steps involved in model training, testing, and selection, and the selected models used for the prediction of P forms are summarized in Fig. S1. We compared our final map of total P concentration with the map of  He at al. (2021). The observed values of total P in the fitting dataset were compared with the respective predicted values in the corresponding gridcells of the estimated maps via the pearson correlation coefficient. The maps presented here were coloured using the scientific colour maps version 8.
Descriptive statistics of the datasets The mean concentration of total P in the fitting dataset across the 108 sites was 284. 13 mg kg -1 (Table  S1 ). The primary mineral and occluded forms of P corresponded to 53. 61 % of the mean total P concentration, followed by the organic P (27. 78 %), inorganic P (11. 9 %) and lastly by the available P (6. 71 %). The descriptive statistics of the features in the fitting dataset are presented in Table  S2. The predictive dataset was obtained after the adequation of several well-known spatial datasets covering the area of interest. The descriptive statistics of the features in the predictive dataset are presented in Table  S3. The areas in Fig  1 2 3 4 ). The area covered by the predictive dataset is approximately 8. 4 x 10 6 km 2 ; of which 8. 2 % was excluded for the prediction of the total P map.
The excluded areas for the final maps of the available, organic, and inorganic P forms were 11. 6%, 11. 4 %, and 9. 3 %, respectively. The excluded areas for each P form overlap in several locations. The intersection of all excluded areas represented 16. 3 % of the covered area in the predictive dataset (hatched areas in Fig. 5 ). The excluded grid cells in the DI analysis have higher values of elevation, slope, TOC and TN and lower values of MAT when compared with the non-excluded grid cells (Fig. S3 ). The predicted values of P forms were consistently higher in the excluded areas (Fig. S4 ). After the exclusion of the grid cells with high DI values, the distribution of the features in the predictive dataset fell approximately within the distributions of the features in the P dataset (Fig.
Model performance and relations between predictive features and target P forms For each modelled P form (total, available, organic, and inorganic P) several models were selected based on minimum thresholds of accuracy (Eq. 1) and performance in a Monte-Carlo cross-validation procedure. Both evaluation metrics were calculated during the training and testing of the random forest regression models with the P (fitting) dataset. We selected 300 models for the prediction of the total P concentration maps (Table  3 ). The models presented a mean accuracy of 77. 37 % (Fig. 1 , left panel) with a mean score (R 2 ) of 0. 7 in the cross-validation (Fig. S7 ). The importance score (Fig. 6 ) shows the features that confer more valuable information and that increase the accuracy of the random forest models in the training/testing phase.
S14 ). In our analysis, the reference soil group has very low values of MDA, indicating that they are not powering the accuracy of the random forest models and, thus, are not good predictors for P forms in the soil in our statistical approach. The predicted values of total P in our map presented a correlation coefficient of 0. 64 (p < 0. 01) when compared with the observed values in the fitting dataset. For comparison, the map of total P of  He et al. (2021)  presented a correlation coefficient of 0. 35 (p < 0. 01).
Discussion We used soil data sampled in 108 plots of the RAINFOR network in the pan-Amazon region (the fitting dataset) to train, test, and select random forest regression models. The target variables were the concentration of total, available, organic, and inorganic P forms in the topsoil. The predictive features in the fitting dataset were latitude, longitude, sand, silt, and clay fractions, mean annual precipitation and temperature, pH, elevation and slope, total N, total organic C, and reference soil group. Using the selected models and a compiled spatially explicit dataset (0. 5° x 0. 5° lat-long) containing 2749 grid cells with estimated values of the predictive features found in the fitting dataset, we constructed estimated maps of the target P forms for the pan-Amazon region.
Soil Phosphorus maps The mean concentration of total P in the fitting dataset (284. 13 mg kg -1 ) shows that the pan-Amazon region is poor in phosphorus when compared with a global mean of 570 mg kg -1  (He et al. , 2021). The Amazonian foreland basins (Fig. S6 ) are marked by relatively higher values of total P. These are Cenozoic sedimentary basins in western Amazon and are under the influence of the ongoing Andean orogenesis uplifting  (Val et al. , 2021 and citations therein)  during the last 10 million years. The regional atmospheric circulation, influenced by orogenic effects, causes high precipitation rates along the Andes foothills in western amazon  (Bookhagen and Strecker, 2008). The transport of primary material enriched with P from the Andes foothills through Amazonian foreland basins and through the lowland catchments of the Amazonas, Solimões, Juruá, and Purús Rivers in the central Amazon (Solimões and Amazon sedimentary basins, Fig  S6 ) results in a relatively higher total P in these regions  (Wittmann at al.
, 2011 , Quesada et al. 2010). In contrast, the sedimentary basins in the lowlands of eastern Amazon that are characterized by approximately 20 millions of years of tectonic stability under the influence of the weathered crystalline outcrops of the Proterozoic rocks of the Brazilian and Guinan shields  (Quesada et al. , 2010). The generated map of total P (Fig. 1 ) resembles the predicted patterns found in the literature  (Val et al. , 2021)  and in comparison with the more recent map of total P  (He et al. , 2021)  our map presented a better correlation coefficient with observed data. However, it is important to note that despite the fact that the dataset used to train our random forest models was a subset of the data used to train an test the random forest model used by  He et al. (2021) , the later was trained using global data aiming to the production of a global map, indicating that training models to restricted areas could be important to optimize the accuracy and predictive power.