dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2015). For the first time, the quality of a MME was 454 measured, and it showed that at the current state of crop model development, especially after 455 improvement, prediction uncertainties and hindcast errors are at the same level. Therefore, given a certain 456 level of squared bias measured with hindcast and applied to predictions, we can assume that predictions 457 with these models are reliable. Since in this work the level of prediction uncertainty was measured using 458 the squared bias for a data set that was also used for calibration, we suggest that for future prediction 459 uncertainty assessments done with this MME, the squared bias of the improved models calculated for the 460 evaluation data sets is used as the reference prediction squared bias. 461.
509 models and discussed the results. PDA, BAK, MJO, MPR, AR, GWW, and JWW provided experimental 540 data. 541 Radiation use efficiency 1. 24 1. 34 m 2 g -1 Maximum specific leaf area 2. 7, LAI < 5 m 2 m -2 2. 2, 5 m 2 m -2 < LAI < 8 m 2 m -2 2. 2, LAI < 8 m 2 m -2 3. 2, LAI < 5 m 2 m -2 3. 0, 5 m 2 m -2 < LAI < 8 m 2 m -2 2. 2 LAI < 8 m 2 m -2 S1. 2 APSIM-Wheat The temperature response function for thermal time accumulation was modified from a triangular to a trapezoidal response curve and the heat stress effect on leaf senescence model was modified from the one proposed by  Asseng et al (2011)  to a linear response including a plateau for T max > 43°C without discontinuity at the threshold temperature (34°C)  (Stratonovitch and Semenov 2015). APSIM-wheat (v7. 5; http://www. apsim. info/) module was re-parameterized against the experimental data from the HSC calibration data set.
Parameters were estimated with the Gauss-Marquardt-Levenberg algorithm using the parameter estimation software PEST  (Doherty and Johnston 2003). The weighted sum of squared errors (WSSE) between observations and model predictions was minimized. Seven phenological data types from each of 28 experiments were used for calibration. These were two to three LAI observations, Sow-Ant, Sow-Mat, GY, AGBM, GNumber and GDM, summing up to a total of 226 data points in the objective function. To account for the different orders of magnitude of the different data types, data from each type were assigned with a different weight in the objective function. This was done to get a similar contribution of each data type to the objective function. Weighting factors were one for Sow-Mat and Sow-Ant, two for AGBM, two for GNumber and 10 for LAI, GY and GDM data.
S1. 7 LPJmL Heat stress effect on leaf senescence was introduced. With daily mean air temperatures above 30°C daily mean air temperature is multiplied with a factor (as) between 1 and 2:   max max max max 1, 30°C 1 30 1, 30°C 40°C 40 30 2, 40°C T as T T T                (S7) which accelerates growth and senescence when applied to the calculation of daily heat units for calculating thermal accumulation (HU sum ): mean sen sum mean sen ( , HU HU , HU HU T as f f hu T f f       (S8) where sen HU f is the fraction of the heat units from sowing to maturity required for the starting of senescence (Table  S6 ). S1. 8 Expert-N-SPASS and Expert-N-SUCROS In both ExpertN-Spass and ExpertN-Sucros models, the daily gross rate of canopy photosynthesis is calculated based on temporal integration of the momentary photosynthesis rates over daytime (10 times per day in case of SPSS and 6 in case of SUCROS) as a function of radiation and air temperature.
510 AM has received the support of the EU in the framework of the Marie-Curie FP7 COFUND People 511 Programme, through the award of an AgreenSkills fellowship under grant agreement n° PCOFUND-GA- 512 2010-267196. PM and DW acknowledge support from the FACCE JPI MACSUR project (031A103B) 513 through the metaprogram Adaptation of Agriculture and Forests to Climate Change (AAFCC) of the 514 French National Institute for Agricultural Research (INRA). SA and DC received financial support from 515 the International Food Policy Research Institute (IFPRI) and the International Maize and Wheat 516 Improvement Center (CIMMYT). FE received support from the FACCE MACSUR project (031A103B) 517 funded through the German Federal Ministry of Education and Research (2812ERA115) and EER was 518 funded through the German Federal Ministry of Economic Cooperation and Development (Project: PARI).
519 EW was funded by the by CSIRO and the Chinese Academy of Sciences through the project 'Advancing 520 crop yield while reducing the use of water and nitrogen'. CM received financial support from the 521 KULUNDA project (01LL0905L) and the MACMIT project (01LN1317A) funded through the German 522 Federal Ministry of Education and Research (BMBF). RPR received financial support from FACCE 523 MACSUR project funded through the Finnish Ministry of Agriculture and Forestry. MPR and PDA 524 received funding from the CGIAR Research Program on Climate Change, Agriculture, and Food Security 525 (CCAFS). CB was funded through the Helmholtz project `REKLIM-Regional Climate Change: Causes 526 and Effects' Topic 9: `Climate Change and Air Quality'. KCK and CN were funded by the FACCE 527 MACSUR project through the German Federal Office for Agriculture and Food (BLE).
The distribution of the root cone angle in the indica panel was Gaussian, but several accessions of the japonica panel (all the bulus from Indonesia and three temperate japonicas from Nepal or India) appeared as outliers with a very wide root cone angle. The data were submitted to association mapping using a mixed model with control of structure and kinship. A total of 15 QTLs for the indica panel and 40 QTLs for the japonica panel were detected. Genes underlying these QTLs (+/-50 kb from the significant markers) were analyzed. We focused our analysis on auxin-related genes, kinases, and genes involved in root developmental processes and identified 8 particularly interesting genes. Conclusions: The present study identifies new sources of wide root cone angle in rice, proposes ways to bypass some drawbacks of association mapping to further understand the genetics of the trait and identifies candidate genes deserving further investigation.
For each panel, an unweighted neighbor-joining (NJ) tree based on a dissimilarity matrix computed using a shared allele index was built using DARwin software  (Perrier and Jaquemout-Collet 2006 ) and the subpopulation attributions of the accessions were projected as colors on the tree (Additional file 4: Figure  S1  and Additional file 5: Figure  S2 ). For the indica panel (Additional file 4: Figure  S1 ), I1 (69 acc. ) included traditional indica varieties from Asia; I2 (45 acc. ) included improved irrigated or upland indica varieties; I3 (16 acc. ) corresponded to a special group of indica accessions from Madagascar grown at moderate to high elevation (1250 to 1750 m), and I4 included a small set (5 acc. ) of aus or boro accessions from eastern India. The last 21 accessions were classified as admixed.
Genotyping by HDRA The HDRA data were recovered from https://ricediversity. org/data/. A subset of accessions corresponding to those used in this study was extracted. The corresponding HDRA ID is given in Additional file 2: Table  S2  and Additional file 3: Table  S3. For several accessions (35 for the indica panel and 20 for the japonica panel), HDRA data were not available. The smaller HDRA panel size explains why the GBS data were used as priority and why the two datasets were not merged. The extracted dataset was submitted to the same filtering as were the GBS data, notably to remove markers that became monomorphic within each panel. The missing data were imputed using Beagle v4. The final dataset was composed of 337,150 and 302,942 SNP markers for the indica and japonica panels, respectively.
Few markers were shared with the GBS dataset. All four datasets under HapMap format (indica and japonica GBS and indica and japonica HDRA) are available for download at http://tropgenedb. cirad. fr/tropgene/JSP/interface. jsp?-module=RICE under study type "genotypes" as a zip file named "Root cone angle". To address discrepancies in names and genotypes between the two genotypic datasets due to differences in seed samples used for the genotyping, a subset of 2000 markers was randomly chosen in each dataset, and two pairs of NJ trees were constructed independently. The GBS and HDRA diversity trees obtained were compared for both subspecies. This procedure enabled the identification of a few accessions that were not classified in the same subspecies or were clearly not the same in the GBS and HDRA panels either because the initial sample was not identical or was heterogeneous or because the sample drifted during the multiplication phase at one site or the other.
Statistics ANOVA was conducted on the experimental data using a mixed model considering the tank effect to be random. Lsmeans were computed for each accession using SAS v9. 2 (SAS Institute, Cary NC, USA). In addition, for each panel, ANOVA was conducted on the accession lsmeans, using sub-populations as the source of variation to assess to what extent the root cone angle was linked to population structure. The admixed accessions were excluded from this specific analysis.
Association analyses For all panels, GWAS was conducted with control of kinship among accessions and panel sub-structure using Tassel v5  (Bradbury et al. 2007 ). As by  Courtois et al. (2013) , a comparison was first conducted between models with kinship alone and kinship and structure together using an Akaike Information Criterion. The conclusion was that the kinship and structure model best fit the data (data not shown). For analyses involving the GBS datasets, the percentages of admixture derived from previous analyses (as explained above) were used to control population structure. The kinship matrix was computed using the centered identity-by-state method. Qq (quantile by quantile) plots and Manhattan plots were obtained from Tassel. A threshold of 1e-04 was chosen to declare a marker significant for the GBS datasets.
As a comparison, Bonferroni correction would lead to a threshold of approximately 5e-06, and the number of independent tests  (Gao 2011 ) computed using the simpleM program would lead to a threshold of 1e-05 for both panels. To confirm that the chosen threshold was reasonable, the q-values corresponding to the false discovery rates (FDRs) were also computed for each of the significant markers using the formula N*Pmax/n, where N was the total number of markers in the dataset, Pmax was the observed p-value and n was the number of markers significant at this threshold  (Storey and Tibshirani 2003). Markers that were significant in an interval of less than +/-50 kb were considered to belong to the same QTL. For analyses involving the HDRA datasets, the number of accessions was 20% and 13% lower than for the GBS data for the indica and japonica panels, respectively.
This difference was important and justified repeating the structure analysis. Principal Component Analyses (PCA) were therefore conducted on the genotypic data; 4 and 6 axes were retained for the indica and japonica panels, respectively, since the number of sub-populations was unchanged compared to that of the GBS data. A threshold of 5e-06, which considers the fact that the HDRA datasets included approximately 20 times more markers than did the GBS datasets, was chosen to declare a QTL significant. By comparison, Bonferroni correction and the number of independent tests would lead to thresholds of approximately 1e-07 and 5e-07, respectively. The q-values were computed the same way as for the GBS datasets. In GWAS, a low sample size relative to the number of markers and association tests can lead to inconsistency in the results.
Detection of candidate genes The position of the significant markers was compared to that of approximately 200 root-related genes in rice from a database established in the framework of the EURoot project (http://euroot. cirad. fr/euroot/JSP/interface. jsp?-module=RICE). Then, the function of the candidate genes in which significant markers were detected was determined using OrygenesDB (http://orygenesdb. cirad. fr/). In addition, genes located in intervals of 50 kb on both sides of the marker were explored. This window size was chosen because it was intermediate between the average marker distance (approximately 25 kb in the GBS datasets and 1. 5 kb in the HDRA datasets) and the distance at which LD decayed to half its initial level (estimated at 100 kb for the indica panel and 150 kb for the japonica panel on average across all chromosomes but much more variable in short-range segments).
GWAS on the japonica panel Because of the discontinuity in the distribution of root cone angle within the japonica panel, data were first analyzed as the indica panel and then subjected to additional analyses. In the first analysis conducted on the whole japonica panel (Table  5 ), the model, although involving both structure and kinship corrections, did not allow for a good control of false positives, as seen in the q-q plot (Additional file 8: Figure  S5 ). Since the distribution was skewed with a long tail in the positive direction, we attempted a log transformation of the data. The  transformation improved the normalization of the model residuals and decreased the significance of the associations but identified the same associations. Among the significant markers, several markers had exactly the same F and P values and, upon verification, were exhibiting exactly the same genotypic pattern, which corresponded to a situation of complete LD (r 2 = 1).
A GWAS was then conducted on the japonica panel with the HDRA dataset (Table  6 ). The same phenomenon was observed as with the GBS data but was more pronounced: long stretches of markers in complete LD on chromosome segments and markers in complete LD located on different chromosomes (up to 828 markers in one case). The analysis enabled the identification of 19 markers (p < 5e-06) corresponding to 18 QTLs. Three of these QTLs (qj01-1 on chr 1, qj07-2 on chr 2 and qj11_2 on chr11) corresponded to QTLs identified using the GBS data. As with the GBS data, all the detected QTLs had a very low MAF. To assess the extent to which these results were influenced by the group of 10 accessions with a very wide root cone angle, we repeated the same analyses on the panel excluding those 10. This time, the model considering kinship and structure accurately controlled the number of false positives (Additional file 10: Figure  S7 ).
A GWAS was then conducted on the reduced japonica panel with the HDRA dataset (Table  8 ). Four markers on chr 2, 3, 7, and 12 were significant but had a relatively high qvalue (from 0. 17 to 0. 36). None of these markers were shared with those detected in the GBS dataset. To identify the loci responsible for the very wide root cone angle, assuming that this trait can be controlled by a major gene, we tested other approaches using only the GBS dataset since phenotypic and genotypic data were available for 10 of the extreme accessions compared with only 8 in the HDRA dataset. First, we looked at markers that were discriminant between the 10 accessions with an open root cone angle and the rest of the panel (157 accessions). Marker S06_21,685,315, corresponding to qj06_02 was found to be the one that discriminated the best the two phenotypic groups.
Analysis of the candidate genes The positions of these 55 QTLs were compared to the position of root-related genes in rice from the EURoot database. No known root gene corresponded to the markers that were found to be significant in the indica panel. For the japonica panel, five genes present in the EURoot database co-localized with significant markers: SHORT POSTEMBRYONIC ROOTS 1 (OsSPR1 / Os0 In italics, initial probability of the markers only significant through sub-sampling. Position 1-position 2: limits of intervals including significant markers in complete LD Finally, the genes included in an interval of +/-50 kb around the markers were analyzed. We found 489 genes with an annotated function, among which 92 were localized within a QTL, and 91 were located in a 10-kb zone around a significant marker (Additional file 12: Table  S4 ).
1997). These results confirm those from  Abe and Morita (1994)  that the link between narrow root cone angle and increased root depth cannot be generalized in rice. Since our panels had been genotyped by two different methods (GBS and high-density chip), we assessed whether the number of markers was a limitation for association detection by comparing the results obtained with the GBS data sets (approximately 15,000 markers) and the HDRA datasets (approximately 300,000 markers). Theoretically, the large increase in marker density should not lead to the identification of many more associations because, in both the indica and the japonica panels, LD decay is much slower than the average distance between markers even in the two GBS datasets  (Courtois et al. 2013; Lafarge et al. 2017 ). However, this statement is based on averages, and locally, large variations or ruptures in LD pattern can be observed, because of recent mutations in some lineages, for example.
In such cases, the HDRA dataset has a greater chance of showing a higher LD between the causative polymorphism and the genotyped markers than does the GBS dataset. Based on our data, the HDRA dataset associations recorded much lower p-values than did those from the GBS dataset (minimum p-value of 6. 3e-10 for the HDRA compared with 2. 5 e-06 for the GBS), but when using a threshold that considered the difference in the number of markers between datasets, little difference in the number of significant associations was observed due to the 20-fold-increase in marker density between the GBS and HDRA datasets. However, one cannot exclude that this lack of difference results partly from the panel sizes, which are smaller for the HDRA datasets. Improvement in resolution can be obtained by an increase in panel size.
In our case, this may have been achieved by pooling the indica and japonica panels. We did not attempt to do so since the conditions for pooling the datasets were not optimal. The experiments with the two panels were conducted independently, and the common controls (IR64 and Azucena) that could have enabled us to bridge the phenotypic datasets registered similar values for root cone angle, making them poor controls for this trait although showing major differences in other root traits. For both panels, the significant markers in the GBS and HDRA datasets pinpointed to similar chromosomal segments in several cases but there were also chromosomal segments where the markers were significant for one dataset and not for the other. One possible reason, besides the difference in panel sizes mentioned above, is the fact that the two datasets were submitted to different ascertainment biases in SNP selection.
The HDRA dataset, derived from sequence data, is enriched in SNPs in and around genes  (McCouch et al. 2016)  while the markers of the GBS dataset were more randomly selected. The proportion of markers with a low MAF of the HDRA dataset was also much higher than that of the GBS dataset (63% and 69% of markers with MAF < 10% for the indica and japonica HDRA panels compared with 38. 0 and 46. 2% for the indica and japonica GBS panels, respectively) although the same filtering procedure was used. Better balanced genotypic classes, as in the GBS dataset, generally provide higher power for association detection. We detected significant associations and tested the robustness of those associations using a resampling technique. Resampling enables the evaluation of the sensitivity of the tests to the specificities of the samples  (Tian et al.
2016 ). In addition, as noted by  Korte and Farlow (2013) , rare alleles that are specific to such small sub-groups of individuals will be in complete LD, which is what we observed with markers with the same p-value on different chromosomes and drove us to Several root-related genes were identified under or close to the significant markers. We screened the literature to determine the biological function of these genes in rice or that of their orthologs in Arabidopsis when no other information was available. First, five genes were found in the EURoot database. Three of them were described as involved in root nutrient transport, in water transport and in root hairs. OsPT15 (Os02g52860) codes for a mitochondrial phosphate transporter  (Liu et al. 2011). OsNIP1;4 (Os06g35930) is an aquaporin that is expressed in leaf blades and in roots involved in water transport in rice  (Sakurai et al.
Acknowledgements This research received funding from the  International Rice Research Institute  in the framework of the  Rhizophen  project for the experiments conducted with the indica panel. This research received funding from the  European Community Seventh Framework Programme   FP7-KBBE-2011-5  under grant agreement no  289300  in the framework of  EURoot  project for the experiment with the japonica panel. The authors thank all the  Cirad  staff who contributed to the various experiments. The authors thank  Chantal Hamelin , from  Cirad , for making the genotypic data available in the Tropgene database.
Calibrated tree. Despite the relatively small size of the dataset, visual inspection of the logs in Tracer v. 1. 6   showed that none of the analyses appeared to be reaching stationarity, with fluctuating parameters and extremely small effective sample sizes for some parameters. We believe this is because of the small size of the morphological dataset (26 characters, compared to a molecular dataset of >16,000 base pairs for most taxa), from which molecular rates had to be calculated for the whole tree. The uncertainty in the tree is also indicated by the very low posterior probability values on nodes that were not constrained. Nevertheless, repeated runs of 10 millions replications consistently yielded the same topology and similar divergence dates (even when certain parameters were changed, such as using a BDSKY tree model).
Material and Methods Material. We used 49 specimens (see Supplemenatry data 1 for information about their origin, age, host institution, and scanning parameters) including Miocene (25 specimens from 7 species), Pliocene (2 specimens from 2 species), and Pleistocene (10 specimens from 8 species) deer from Europe, Northern America, and Asia, and extant (12 specimens from 10 genera and 12 species) deer. Our dataset therefore comprises more than half of the current cervid diversity (for a total of 17  64  , 18  59  , or 19 7 genera). The fossil species were selected in order to encompass two of the oldest known ruminants possessing deciduous cranial appendages (Lagomeryx parvulus and Procervulus praelucidus  22  ) and most of the Plio-Pleistocene genera, which are part of the Cervidae radiation during glacial episods.
Geometric morphometrics analyses. Left bony labyrinths were preferably selected when available. If not, the right one was included for analysis using the reflect application of Landmark Editor 3. 6 software  65. Digitizing of the specimens was also performed using this software. 77 curves of 10 semi landmarks and 1 landmark were digitised on the surface of the specimens following the protocol described in Mennecart and Costeur  29. Shape variation in bony labyrinth morphology (disparity and similarity) was studied using the geometric morphometrics methods implemented in MorphoJ 1. 06d software  66. Principal component analysis is used to visualise the overall shape variation among specimens. It encompasses 3 species of Procervulinae (11 specimens), 1 species of Dicrocerinae (3 specimens), 6 living species of Capreolinae (7 specimens), and 6 living species of Cervinae (6 specimens) for which we have a phylogenetic control  7, 30.