dataesr/scientific-paragraphs-categorization huggingface dataset:
Description of the HPAI H5N1 mitigation system in Cambodia Our knowledge of the HPAI H5N1 mitigation system in Cambodia was based on bibliographic data  [26, 27]  and a field study conducted in April 2012 in the Cambodian provinces of Kompong Cham and Takeo. In the course of this study, we interviewed key stakeholders of the system to collate different points of view on the functioning of the system. Key stakeholders consisted of 21 farmers, 21 designated Village Animal Health Workers (VAHWs), 6 district veterinarians, 3 province-level veterinarians and 3 NaVRI (National Veterinary Research Institute) personnel. The VAHWs are village volunteers, usually one per village, who have been trained to recognize, treat and report certain animal diseases to the health authorities, in particular cases of HPAI H5N1.
Acknowledgements We extend our thanks to the NaVRI members as well as the veterinarians and village volunteers of  Takeo  and  Kompomg Cham  provinces in Cambodia, for their contribution to this study. We acknowledge the assistance of  Nget Kiry ,  Sophie Baron  and  Kunthy Nguon  in interviewing the Cambodian stakeholders, and  Dr Mak Chanthol  in commenting our models. We also thank the  CIRAD GREASE Network  for its support. Many thanks to  Chris Dickson  for the translation of this article into English. This study was funded by the  CIRAD 's REVASIA programme and the  CIRAD AGIRs Unit.
Hydro-sedimentology development is a great challenge in Peru due to limited data as well as sparse and confidential information. This study aimed to quantify and to understand the suspended sediment yield from the westcentral Andes Mountains and to identify the main erosioncontrol factors and their relevance. The Tablachaca River (3132 km 2 ) and the Santa River (6815 km 2 ), located in two adjacent Andes catchments, showed similar statistical daily rainfall and discharge variability but large differences in specific suspended-sediment yield (SSY). In order to investigate the main erosion factors, daily water discharge and suspended sediment concentration (SSC) datasets of the Santa and Tablachaca rivers were analysed. Mining activity in specific lithologies was identified as the major factor that controls the high SSY of the Tablachaca (2204 t km 2 yr -1 ), which is four times greater than the Santa's SSY.
(2001)  show that, at the Andes scale, topographic characteristics match the mean annual precipitation and the theoretical erosion index intensity (product of the local slope with the upstream rainfall amount). These authors suggest that this correlation indicates that climate is a first-order factor for the topographic evolution of the Andes. Another approach is to analyse modern suspendedsediment yield (SSY) databases. Due to the limited amount of publicly available SSY data from Andean catchments, only a few studies have reviewed the relation between Published by Copernicus Publications on behalf of the European Geosciences Union. erosion factors and SSY there (e. g. Aalto et al. , 2006; Armijos et al. , 2013; Laraque et al. , 2009; Molina et al. , 2007 Molina et al. , , 2008;; Pépin et al.
, 2010; Restrepo et al. , 2006a; Guyot, 1993). In the northern Andes, mean annual runoff explained most SSY variability in the Magdalena River catchment and sub-catchments  (Restrepo et al. , 2006b). However, this study only explored hydrologic, morphometric and climatic factors and cannot explore vegetation, soil properties and the effect of land use because of a lack of information about these parameters. Conversely,  Aalto et al. (2006)  observed no relation between runoff and SSY in a database of 47 Bolivian catchments. On the other hand, lithology and slopes had the highest correlations with SSY in the Bolivian Andes. This study, however, mainly focused on geomorphic, hydrologic and lithologic parameters without any information about vegetation or land-use parameters for connected subcatchments.
Pépin et al. (2010)  performed a complete study of SSY in 66 Chilean catchments of similar size along the Andes, from extreme northern Chile to southern Patagonia, covering a wide range of climate, slopes and vegetation. This study showed that SSY had a linear relation with slope and runoff above and below threshold values related to vegetation cover, respectively. Lithology and seismicity have also been explored but have not given reliable results due to the nonexhaustiveness of these data in Chile. On the eastern side of the northern and central Andes, SSY had the highest positive correlation with rainfall variability and negative correlation with mean annual rainfall  (Pépin et al. , 2013). At the hillslope scale (< 1 km 2 ),  Molina et al. (2007 Molina et al. ( , 2008) )  showed that SSY is well correlated with cover, soil types and road networks, based on a database of 37 small sub-catchments of the Paute River (Ecuador).
Despite the particular climatic configuration of the Andes, these studies cannot give a clear view of the relative dominance of erosion-control factors. Actually, a nonnegligible area of the western Andes is not documented due to the scarcity of available and reliable data on SSY there. Among other factors, the rugged Andean piedmont topography and extreme east-west climatic gradients with large rainfall-event variability are some characteristics of this region often proposed as main erosion-control factors. Therefore, collecting SSY data from the west-central Andes is of primary interest to deepen understanding of erosion factors in the Andes. In this study, a new daily SSY dataset from the central Andes in Peru was analysed during collaboration between the CHAVIMOCHIC (http://www. chavimochic.
gob. pe/) and HYBAM (http://www. ore-hybam. org/) projects. Reliable suspended sediment concentration (SSC) data have been collected since 1999 by the Peruvian irrigation CHAV-IMOCHIC project. It monitors sediment in the lower section of the Santa River catchment at three stream discharge and sediment load gauging stations. The Santa River catchment, located in the west-central Andes, has strong altitudinal gradients, with high topographic contrast with elevations ranging from sea level to the highest point in the central Andes (Nevado Huascaran; 6768 m a. s. l. ). The seasonal and low vegetation cover, poorly consolidated lithology, and weather all change significantly over relatively short distances, and the east-west rainfall gradient ranges from 151 to 1115 mm yr -1 (1998-2010). In addition, there is intense human activity such as small-and large-scale mining of coal, metal and aggregates distributed from the coast to the highlands  (Morera, 2010).
In analysing this exceptional SSC dataset, we identify two adjacent catchments that have significant differences in their SSY despite having similar climatic and hydrologic contexts. This is a good example for improving understanding about which factors control the magnitude and frequency of SSY from the west-central Andes to the Pacific coast, with a special focus on (i) spatial differences in sediment production at the basin scale (few thousand km 2 ), (ii) non-climatic erosion factors such as mine activity in specific lithology, and (iii) the relevant resolution of maps necessary to define erosion factors in the central Andes. In addition, the SSY of the Santa River is compared to SSY observed in the Andes range from 35 • S to 10 • N.
Land-use parameters and mining activity Land-use data were processed from high-resolution (30 × 30 m) Landsat-7 ETM images downloaded from the US Geological Survey's Earth Explorer (http://earthexplorer. usgs. gov/). We downloaded three image mosaics from June to July 2006 (a period of fewer clouds and relative stability in vegetation cover) encompassing the entire study area and then analysed them by classifying spectral bands 2, 3 and 4  (Adams et al. , 1995). We first evaluated the main land-cover areas using GPS points that were well distributed throughout the catchment, such as forests, crops, slide areas, rocks, mines, towns, glaciers, water bodies and areas of dark soil due to exposed coal. We then performed supervised classification of land cover with ERDAS software according to  Göttlicher et al.
Slope and geomorphological characteristics Several datasets with geo-information are currently available, making it possible to derive catchment parameters from Shuttle Radar Topography Mission (SRTM) 3-arcsecond The basic configuration of the geomorphology, such as the mean slope, surface area, river networks, and height differences within each catchment, were calculated using GIS tools. Next, spatial variability in local slope of the two subcatchments was analysed. The middle-altitude portion of the Santa sub-catchment is a deep, steep-walled canyon about 15 km long, at the bottom of which is a narrow verticalwalled slot up to several hundred metres deep called the "Cañon del Pato"  (Ericksen et al. , 1970). Higher elevations, starting from the central part of river valleys and extending upstream, have platforms that become planed surfaces near river headwaters that form flat areas (68 % of the whole area); in most cases, low slopes (0-17 • ) are most common.
, 2009; Condom et al. , 2012). In mountain catchments, especially in developing countries, significant spatial and temporal gaps in ground-based climate records exist  (Scheel et al. , 2011). We obtained precipitation estimates from the TRMM Multi-satellite Precipitation Analysis (TMPA) level-3 product 3B43-7V (1998-2012, Fig. 4 ), from the Goddard Earth Sciences Data and Information Services Center product (http://mirador. gsfc. nasa. gov). TMPA is a combined from several (10) data providers  (Huffman and Bolvin, 2013). This dataset is a calibrationbased sequential scheme for combining precipitation estimated from multiple satellites, as well as gauge analyses where feasible, to create a new monthly precipitation product on a 2. 5 • × 2. 5 • grid  (Huffman et al. , 2007). The TMPA products were processed at catchment scale (Fig.
4 ) to estimate mean monthly precipitation on the two catchments and capture its spatial variability. Two in situ rainfall stations with daily rainfall data, Yungay (2537 m a. s. l. ; -9. 14992 • S, -77. 75103 • W) for the Santa sub-catchment and Cabana (3300 m a. s. l. ; -8. 3531 • S, -78. 00201 • W) for the Tablachaca sub-catchment, were used (Fig. 1 ). These precipitation data were recorded by the Peruvian Institute of Meteorology and Hydrology (SENAMHI). Spatial variability in monthly precipitation was evaluated with the TRMM dataset, considering two data subsets per sub-catchment: pixels representing areas > 3800 m vs. those of areas < 3800 m. For 2002-2012, mean annual precipitation was similar for each station (∼ 850 mm yr -1 ; Fig. 4 ), and each dataset correctly showed an October to April wet season and a May to September dry season.
Comparison of monthly precipitation from each data subset showed no major differences (< 20 mm month -1 ). Cabana and Yungay stations were directly compared with the TRMM catchment precipitation datasets, suggesting that Cabana and Yungay data are representative of mean precipitation in Tablachaca and Santa sub-catchments, respectively. Consequently, the two time series were processed to calculate a probability density function (PDF, Fig. 6 ), according to Andronova and Schlesinger (2001) (see Sect. 3. 2 for the analysis).
New dataset: outflow and sediment yield Accurate estimates of SSY depend on effective monitoring strategies  (Duvert et al. , 2011). This study uses an extensive database as one input to increase understanding of relations between SSY and environmental variables at the   catchment scale in the Andes. Since 1999, the Peruvian irrigation CHAVIMOCHIC project has been performing sediment flux monitoring. Daily water discharge (Q) readings are taken by water-level recorder equipment, and are based on the relation between the gauging and level readings; a water discharge curve is generated. Samples of SSC are taken twice a day (06:00 and 18:00) at the Condorcerro, Santa and Tablachaca stations. As a result, an exceptional unpublished database is available with two SSC samples per day (Fig. 5 ) and instantaneous water-level recorder readings of Q.
Sample SSC measurements available at the gauging stations were evaluated considering an average resolution of one sample per day for each station. A lack of SSC data for a particular day did not necessarily constitute a "true" data gap. One can expect the use of a sediment rating curve (SSC vs. Q) to give plausible results in such cases, unless SSC is entirely unrelated to Q. Only if the flow record was also missing were data considered missing. After that, the percentage of available SSC data was calculated for each month. The entire observed dataset at Condorcerro station contained 2. 5 % SSC data gaps, 1. 7 % corresponding to the rainy season. The entire Tablachaca station dataset contained 26 % SSC data gaps, 12. 4 % corresponding to the rainy season, while the entire Santa station dataset contained 35.
4 % SSC data gaps, 15. 4 % corresponding to the dry season. SSY data gaps in the Condorcerro station dataset were filled using the sediment rating curve equation (Fig. 7 ) (n = 11 467; R > 0. 9; p < 0. 0001). Gaps in the Tablachaca and Santa SSY datasets were filled in two steps: (a) a balance in SSY between Condorcerro, Tablachaca and Santa stations (considering Condorcerro as a junction), as long as there was only one gap for the same interval of time in any station, and (b) averaging the SSY from the day before and the day after the day of the data gap. In the end, both Tablachaca and Santa stations had 19 % data gaps in the treated dataset. The annual hydrological cycle exhibits high seasonal contrast and permanent streamflow during the dry season in both sub-catchments due to glacier melt and groundwater contribution (Fig.
Suspended load/bedload partitioning In this study, all measurements and analysis focused on suspended load because of the lack of any data about bedload. Our results for SSY are therefore underestimations of the total sediment yield. Following the review of  Turowski et al. (2010) , there is no relevant empirical law to define this partitioning from watershed characteristics. Currently the percentage of bedload in total sediment yield varies from 0 to 100 % according to the river context. For Andean catchments similar to the Santa and Tablachaca catchments, there are no published data about bedload yield. More to the north of Peru, in the Catamayo-Chira catchment, total sediment yield has been quantified with bathymetric monitoring of the Poechos reservoir  (Tote et al. , 2011). Unfortunately, these data cannot be compared to ours because the climate of the Catamayo-Chira catchment is impacted more by El Niño and La Niña events than that of the Santa and Tablachaca catchments, and because differences in the SSY vs.
SSC control factors One of the main results of this study is the large difference in SSY between Tablachaca and Santa sub-catchments. The SSY of the Tablachaca sub-catchment is four times larger than that of the Santa sub-catchment despite the former having relatively higher mean annual rainfall, water discharge and slopes. Better understanding of this difference in erosion rates required a fine analysis of plausible erosion factors. Using a variety of datasets and methods, we analysed correlations between criteria, empirical relations and spatial distribution of rainfall rates, daily water discharges, SSC, slope frequencies, lithology, and land uses. Combining these results led to the following interpretation. The rating curves for the Tablachaca and Santa rivers are well defined, and the sensitivity of SSC to Q, represented by the exponent b of the rating curve, is the same for both catchments (Fig.
No specific hydrologic conditions during the field campaign can explain the high SSC values observed on a few upstream segments of the Tablachaca network. SSC decreases downstream due to less concentrated inputs in downstream reaches but remains relatively high until the confluence with the Santa River, with a value of 13. 3 mg L -1. This SSC dataset highlights the coupled impact of mining and lithology on SSC and explains the high values found for the Tablachaca River. The mineral composition of the highest suspended sediment concentration was measured in the X-ray laboratories of the Geological, Mining and Metallurgical Institute (INGEMMET). Results show that it is composed of orthoquartzites, siltstones, sandstones, shales and coal and matches mainly the Chimu Formation (code 2a)  (Carrascal-Miranda and Suárez-Ruiz, 2004).
Chimu lithology, covering 11 % and 1 % of the Tablachaca and Santa sub-catchment areas, respectively, represents the main lithologic difference between these sub-catchments. Furthermore, the highest incidence of mining is observed on Chimu (code 2a) lithology in the Tablachaca sub-catchment. Because the rainfall, hydrology, land-use and slope data cannot explain the differences in erodibility, and because SSC spatial distribution is well correlated with lithology and mining activities, the former emerge as the main control factors to explain this difference. The unusually high SSC observed at the outlet of the Tablachaca River for a wide range of specific water discharge values shows that large amount of sediment is available on the Tablachaca's hillslopes and river bed. Indeed, soft material coming from landslides, continuous delivery of fresh mine tailings and exploitation of river bed material can be seen at many places on both channel networks.
Strong, unexplained spatial differences in SSY could lead to misinterpreting analysis of erosion-control factors at a global scale. We illustrate this point by compiling SSY vs. runoff data from the central and northern Andes from previous studies  (Figs. 11 and 12). From this dataset there is no evidence of runoff controlling SSY since there is no clear relation between them, and the highest SSY are found in the most arid regions. There is no doubt, however, that runoff is one of the main factors controlling SSY. This is because water discharge is intrinsically related to river water velocity (i. e. the main factor causing mechanical shear stress and sediment transport) and because rating curves between SSC and Q in many of these Andean catchments are statistically significant despite data dispersion.
Therefore, using annual mean values for runoff and SSY mixes dependency on different factors and can lead to erroneous interpretations. Furthermore, this study emphasises how SSY at the 10 000 km 2 scale can be controlled by local processes such as mines on a small percentage of the drainage area with specific lithology domains. If locally efficient erosive processes are suspected, such as in the western Andes, where mines are densely aggregated, analysis of erosion-control factors must be done with high temporal and spatial resolution data to understand SSY datasets better.
Acknowledgements. This work was initiated as part of a Ph. D. dissertation at  La Molina Agrarian National University ; it is financially supported by a doctoral research scholarship from the  Science and Technology Programme  (FYNCYT, www. fincyt. gob. pe). The authors are grateful for the funding through  GREAT ICE  team and the HYBAM observatory (www. ore-hybam. org), a join  French-Peruvian program , associating the  French Institut de Recherche.
Simulation QTL and phenotypes were simulated for different heritabilities and numbers of QTL. An existing real dataset was used as a base for the simulation. It consisted of 4387 genotyped French Holstein bulls, with a pedigree of 12 142 individuals. The genotypes were obtained with the Illumina Bovine SNP50 Bead Chip® by Labogena. After removal of SNPs with a minor allele frequency below 5%, 38 277 SNPs were retained for analysis. Simulations were carried out in nine scenarios, with heritability (h 2 ) of 0. 1, 0. 4 or 0. 7 and 10, 100 or 1000 biallelic QTL, in both the full dataset and a subset consisting of 1500 randomly selected individuals from the full dataset. Table  1  summarizes the nine simulation scenarios. For the scenarios with 100 QTL and a heritability of 0. 4 or 0. 7, the simulation was run 11 times.
Prediction of the status at the causative mutation Prior to analysis, genotypes were phased in order to define haplotypes. Phasing was done using DagPhase  [26] , accounting for both family structure and population linkage disequilibrium. To predict the status of an individual for a simulated causative mutation, first the estimated effects of all markers in an identified QTL interval were summed up for each haplotype that the individual carried. Second, the absolute value of the difference between the estimated effects of an individual's two haplotypes was used to group individuals by partitioning around medoids (PAM)  [27] , as implemented in the fpc R-package  [28] , where the number of clusters (n) is estimated on the basis of optimum average silhouette  [29]. The PAM algorithm was as follows: 1.
Posterior distribution of π The posterior distribution of the proportion of markers included in the Bayes Cπ model (1-π) for the full dataset is shown in Figure  1. Only in the scenarios with 10 or 100 QTL and a heritability of 0. 4 or 0. 7, did the posterior distribution of π show a strong peak. In the other scenarios, values taken by π ranged from 0 to 1. In the subset data, a clear peak in the posterior distribution of π was only observed with a heritability of 0. 7 and 10 or 100 QTL. For scenarios without a clear peak, the average PIP was much higher than for scenarios with a strong peak. As shown in Figure  2 , for a scenario without a strong peak, the average PIP was high and the peaks were much smaller and broader for Bayes Cπ than for Bayes C. Table  2  shows the average number of markers included in the model in the full dataset.
Number of records, interval size and number of detected QTL The number of records had a strong influence on the accuracy of QTL detection. Figure  3  shows the number of false positives for both the full dataset and the subset. The number of false positives was higher for the subset than for the full dataset. The difference was largest in the scenarios with 10 QTL and a heritability of 0. 4, which had only one false positive with the full dataset but five false positives with the subset. Figure  4  shows the effect of interval size on the number of false positives. In the scenarios with 1000 QTL, the genome was saturated with QTL, and there were hardly any intervals without a QTL. As we were interested in identifying the largest QTL, we redefined the number of false positives as the number of detected intervals without QTL or with only small QTL for these scenarios.
Discussion The accuracy of QTL mapping was affected by heritability, number of QTL, and number of records. In the subset data, the number of false positives was very high in all scenarios, demonstrating the need for a large dataset. With a low heritability, the QTL effects were smaller than with a higher heritability, making it more difficult to accurately locate the QTL and thus resulting in a greater number of false positives. Therefore, better results will be obtained when progeny-based evaluations are used rather than individual phenotypes, since the heritability of progeny means is higher. This strategy based on daughter-yield-deviations or deregressed estimated breeding values is quite frequently used in dairy cattle  [30]. When the number of QTL to explain a given amount of genetic variance increases, the individual QTL effects become smaller and the QTL thus become more difficult to detect and locate.
Acknowledgements IB benefited from an  Erasmus-Mundus  fellowship and a grant by  Apisgene , within the framework of the  European Graduate School in Animal Breeding and Genetics. This work was part of the  "Rules and Tools " project, financed by the  French National Research Agency  ( ANR-09-GENM-002-01 ). Genotype data originated from the  Cartofine  project funded by  ANR and Apisgene.