dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2016). We Note. PFT area, LAI RMSE, and V cmax25 are calculated for year 2010. Survival percentage is for year 1850. For V cmax25 , data are only included for plants that survive (i. e. , annual maximum LAI > 0. 01 m 2 /m 2 ). For V cmax25 , CLM4 and CLM4. 5 parameter values are prescribed. V cmax25 is a prognostic quantity in CLM5. All data shown are from GSWP3v1-forced BGC simulations. Note that survival rates for CRUNCEPv7-forced simulations are generally within 5% of those with GSWP3v1. N = needleleaf; B = broadleaf; E = evergreen; D = deciduous; T = tree; S = shrub. a LAI observations are from MODIS. b V cmax25 observational estimates are from  Kattge et al. (2009). Note that TRY database reports two values for tropical trees, 29 for trees on oxisol soil, and 41 for trees on nonoxisol soils.
, 2009; Niinemets & Keenan, 2014) , could in principle increase interannual variability of these fluxes; thus, the decrease in variability seen here is not necessarily indicative of a structural degradation or inappropriate PHS parameters. The partitioning of evapotranspiration into transpiration, canopy evaporation, and soil evaporation is a key emergent process simulated by land models, essential to assess ecosystem WUE  (Lawrence et al. , 2007). In Figure  10 , we show the transpiration fraction from each model compared to estimates of transpiration fraction from the Water, Energy, and Carbon with Artificial Neural Networks data set (WECANN,  Alemohammad et al. , 2017 , available at https://gentinelab. eee. columbia. edu/content/datasets). In prescribed vegetation configurations, CLM5SP shows better agreement with WECANN transpiration fraction than either CLM4SP or CLM4.
5SP, especially in the tropics. Globally, the contribution of soil evaporation to ET is diminished in CLM5 relative to CLM4 and CLM4. 5, resulting in a higher percentage of ET Journal of Advances in Modeling Earth Systems coming from transpiration (CLM4SP and CLM4. 5SP 53%; CLM5SP 60%, Table  4 ), in line with recent isotopic data estimates of 61% ± 15%  (Jasechko et al. , 2013; Schlesinger & Jasechko, 2014). However, in prognostic vegetation mode, biases in simulated LAI lead to poorer agreement with WECANN for ET partitioning for all model versions. In particular, low LAI biases for tropical deciduous trees (Figure  S2 ), especially in the Sahel and southern Africa, appear to correlate with low biases in transpiration fraction, though errors in the observations. Table  4  shows global percentages for transpiration, soil evaporation, and canopy evaporation for the CLM versions.
10. 1029/2018MS001583 It is also relevant to note the impact of forcing data set on snow density and ALT simulations. Snow tends to be less dense with CRUNCEPv7 forcing than with GSWP3v1 forcing. Taken in isolation, this should lead to shallower ALT with GSWP3v1 forcing, but instead ALT is generally deeper which appears to be due largely to greater downwelling longwave and shortwave radiation in GSWP3v1 forcing data. The large differences in simulated permafrost distribution and ALT between the two forcing data sets reveal an important aspect of uncertainty in permafrost modeling (which propagates to uncertainty in modeled soil carbon stocks, as discussed below). ILAMB output indicates that downwelling longwave radiation, downwelling solar radiation, and humidity variables all score significantly higher across the Arctic land domain with GSWP3v1 (other forcing quantities are roughly equivalent across these two forcing data sets) which suggests that for permafrost studies, GSWP3v1 forcing may be more appropriate.
5, and CLM5, respectively. The divergence in C accumulation between runs with different forcing data sets arises early in the period, mainly prior to 1950, when CO 2 fertilization would have been relatively small and LULCC fluxes dominate. This implies, then, that LULCC C fluxes can differ substantially even within a model version forced with exactly the same LULCC time series but under different estimates of historical climate forcing. We hypothesize that the simulated preindustrial (year 1850) vegetation C stocks and their regional distribution can impart a strong influence on historical LULCC C fluxes. Finally, while the long-term land carbon accumulation agrees better with observed estimates, which are derived from atmospheric CO 2 and ocean C inventories, the interannual variation in land C accumulation appears to be degraded in CLM5 (larger low bias in variability), based on a comparison of interannual variability of atmospheric CO 2 simulated from the CLM fluxes compared to that observed (see ILAMB CO 2 diagnostics).
Urban To evaluate behavior of the updated urban model and building properties data, observations from five urban flux tower sites and a global anthropogenic heat flux (AHF) data set were used. In simulations described in  Oleson and Feddema (2019) , radiative and turbulent fluxes, surface temperatures, and AHF were found to be generally improved compared to the previous version. The simulation of global and regional AHF is also significantly improved, mainly due to the new building energy model. For example, large positive biases in AHF over the United States and Europe, evident in the previous model version, are reduced such that simulated values are now within 1% and 11% of observations, respectively. The increased simulation fidelity and new capabilities of the model should enhance its utility for research into the combined effects of urbanization and global climate change.
We stress, however, that model users should consider improvements or degradation identified in ILAMB or other metrics presented here with caution due to observed data limitations related to data scale applicability, measurement uncertainties, inconsistencies across multiple observational data sets for one or more variables 10. 1029/2018MS001583 Journal of Advances in Modeling Earth Systems (e. g. , water and energy budgets derived from the available observationally based ILAMB data sets do not close), as well as limitations in the metrics included in ILAMB. Improved methods within ILAMB to account for observational data uncertainty are critical and are a priority for the ILAMB project. An improvement or a degradation for a particular variable or metric does not on its own imply that the model is suited or not suited for research related to that particular variable.
For example, ILAMB indicates that snow water equivalent is degraded in CLM5 relative to CLM4. This apparent degradation occurs despite several mechanistic improvements to snow physics that have been introduced between CLM4 and CLM5. The lower ILAMB score for snow water equivalent for CLM5 could indicate a real model snow simulation performance degradation (due to structural or parametric problems introduced during development from CLM4 to CLM5), but it could also potentially be attributed due to inaccuracies in the forcing data or biases in the observed data set used in ILAMB or limitations in the ILAMB metrics themselves. Consequently, CLM5 users interested in applying the model for research into snow processes will need to balance knowledge of the snow physics and snow physics structural advances against the ILAMB score decrease and against their own assessment of snow simulations to decide whether or not the model is "fit-for-purpose.
" More explicit process representation enables new types of observations to be applied for evaluation of CLM. For example, since CLM5 implements prognostic, rather than prescribed, leaf photosynthetic traits, observations of V cmax25 and J max25 can be used as a means for assessing the model. Similarly, the introduction of plant hydraulics opens up the potential to employ several observational quantities that were previously not applicable to CLM including mid-day stomatal conductance, leaf water potential, and sap flow. This list could continue, but in general, the expansion of CLM science to more realistically represent physical and ecological processes opens also new opportunities to evaluate the model with diverse observational data sets. Identifying, developing, and applying these and other new data products to constrain the more realistic Open-source development of CLM is ongoing (https://github.
Modified dependence of fire occurrence and spread on fuel wetness Lakes Updated lake model including deep and shallow lakes Urban Multiple urban density classes • Building space heating and air conditioning • Heat stress indices 10. 1029/2018MS001583 Journal of Advances in Modeling Earth Systems scenario data provide annual land use descriptions at 0. 25°resolution from the year 850 to 2100. The LUH2 data are generated from the History Database of the Global Environment (HYDE version 3. 2, Goldewijk et al. , 2017) for 850-2014 and from Integrated Assessment Model teams for multiple alternative scenarios of the future for 2015-2100. The LUH2 time series describes annual changes in primary and secondary forest and nonforest land units, along with five crop groups, managed pasture, rangeland, and urban areas.
Figure12. Metric for apparent soil carbon turnover time versus mean air temperature, as in Koven et al. (2017)  for observations and CLMBGC model versions. Turnover time is calculated in observations and models as ratio of mean carbon stocks (SOM) over climatological annual mean carbon inputs (NPP). Each dot represents one grid cell, color coded by mean annual precipitation. The best fit regression curve for the observational data with 50% prediction intervals is shown as black lines for the models. RMSE represents the agreement with the best fit curves. See Koven et al. (2017)  for full description of this metric. Observations for soil organic matter (SOM) are merged from Harmonized World Soil Database (Fao/Iiasa/Isric/Isscas/Jrc, 2012) and Northern Circumpolar Soil Carbon Database (Hugelius et al.
Acknowledgments We would like to thank the reviewers for their insightful comments and helpful suggestions that improved the clarity and presentation of the manuscript. The  CESM  project is supported primarily by the  National Science Foundation (NSF). This material is based upon work supported by the  National Center for Atmospheric Research , which is a major facility sponsored by the  NSF  under Cooperative Agreement 1852977. Computing and data storage resources, including the Cheyenne supercomputer (doi:  10. 5065/D6RX99HX ), were provided by the  Computational and Information Systems Laboratory  (CISL) at  NCAR. D. M. L. was supported in part by the  RUBISCO Scientific Focus Area (SFA) , which is sponsored by the  Regional and Global Climate Modeling (RGCM) Program in the Climate and Environmental Sciences Division (CESD) of the Office of Biological and Environmental Research in the U.
•• Modified dependence of fire occurrence and spread on fuel wetness Lakes Updated lake model including deep and shallow lakes Urban Multiple urban density classes • Building space heating and air conditioning • Heat stress indices 10. 1029/2018MS001583 Journal of Advances in Modeling Earth Systems scenario data provide annual land use descriptions at 0. 25°resolution from the year 850 to 2100. The LUH2 data are generated from the History Database of the Global Environment (HYDE version 3. 2, Goldewijk et al. , 2017) for 850-2014 and from Integrated Assessment Model teams for multiple alternative scenarios of the future for 2015-2100. The LUH2 time series describes annual changes in primary and secondary forest and nonforest land units, along with five crop groups, managed pasture, rangeland, and urban areas.
5BGC 113 (137) 47 0. 48 66 43 107 (100) 2,379 450 (432) 879 (862) CLM5BGC 125 (119) 49 0. 43 70 42 112 (103) 2,574 492 (460) 1,057 (1,040) c Soil C to 1-m depth observed estimate from Harmonized World Soil Database (Fao/Iiasa/Isric/Isscas/Jrc, 2012).
ASSEMBLY The linked reads were assembled with Supernova  (Weisenfeld et al. , 2017)  version 1. 0 ("supernova1_complete") and twice with version 2. 0. 0, the first with all reads ("supernova2_complete") and the second with the -maxreads parameter set to use only reads corresponding to a 56X genome coverage ("supernova2_reduced"), following the software best practices. The assembly metrics were calculated using the assemblathon_stats. pl script  (Bradnam, 2012). To obtain k-mer spectrum graphs (shown in Supporting Figure  S1 ), k-mers were counted with Jellyfish mer counter v. 2. 1. 1  (Marcais and Kingsford, 2011 ; parameters: count -C -m 21 -s 100 M). The K-mer Analysis Toolkit v. 2. 4. 1 (KAT;  Mapleson et al. , 2017)  was used to compare k-mers between raw reads and assembly (kat comp; parameters: -m 21) and to draw plots (kat plot spectra-cn; parameters: -w 30 -l 20 -× 100 -dpi 300).
The assembly gene content was assessed with BUSCO version 3. 0. 2 (Waterhouse et al. , 2017 ) using actinopterygii_odb9 as reference data set. The three P. occidentalis genome assemblies gave different statistics (Supporting Table  S1 ). Depending on the software version and method the P. occidentalis genome assembly was comprised of 7,753, 7,444, and 15,410 scaffolds corresponding to 19,800, 15,621, and 23,570 contigs having a scaffold N50 of 2. 77, 0. 99, and 1. 54 Mb, and scaffold L50 of 60, 178, and 103, respectively. The total assembly lengths are 613, 608, and 725 Mb; i. e. , within the range of the Feulgen densitometry estimated size of 680 Mb for this species  (Cimino, 1974). The unknown base assembly fraction is low, corresponding to 3. 26, 1. 52, and 1. 62% of the scaffold lengths, respectively.
ANNOTATION Gene prediction was achieved with a pipeline that collects and synthesizes evidence from genes and intergenic regions, repeat identification, homology annotation, RNA-seq read mapping, and de novo gene prediction. For each homology annotation, transcript evidence and ab-initio gene model annotation, two independent threads were run. Before starting the annotation process, we used the Actinopterygii odb9 database of BUSCO  (Simao et al. , 2015)  to train AUGUSTUS 3. 2. 3 (Stanke et al. , 2006). Repeat elements were first identified de novo using RepeatModeler  (Smit et al. , 2013 (Smit et al. , -2015) )  (http://www. repeatmasker. org/). The result, along with a fish-specific repeat database (unpublished) and the database from  Shao et al. (2018) , was used as a custom library for RepeatMasker to identify repeats in a comprehensive way.
The Gila topminnow genome shares with other poeciliid genomes (i. e. , southern platyfish, guppy, and Amazon molly) an ancient wave of transposable element (TE) expansion. Nonetheless, the more recent TE expansion that is typically seen in poeciliids appears to have started relatively more recently in the Gila topminnow, where its peak in the most recent elements implies that this expansion is probably ongoing. Detailed studies on TE expression and activity in the Gila topminnow will yield insights into how TEs shape the evolution of their host genomes. In total, 41,501 genes were predicted of which 10,625 were removed because they were deemed of low-quality [i. e. , no hit in BUSCO, Swissprot, and Pfam database, failing to present an intact structure (both start and stop codon predicted) and with Augustus score <80).
Among the 30,976 retained genes, 27,947 (90. 22%) were annotated with start and stop codons, 28,141 (90. 84%) have BLAST hit to database Swiss-Prot (www. uniprot. org), 24,912 (80. 42%) were suggested by InterProScan (http://www. ebi. ac. uk/interpro/interproscan. html) to contain functional protein domains, and 28,722 (92. 72%) were supported by RNA-seq reads. The mean coding sequence length in the retained genes is 1,463 bp and the longest is 54,050 bp. A quality assessment by BUSCO analysis revealed 95. 4% complete conserved Actinopterygii genes, 3. 6% fragmented and only 1. 0% missing genes (Supporting Table  S1 ). The annotation process thus increased the quality of all BUSCO parameters evaluated in this assembly.
FUNDING This work was supported by  NSF   DEB 9902224  to  R. C. Vrijenhoek  and  MM. MScha  was supported by the  Hagler Institute of Advanced Study of Texas A&M University  and funds provided by the  Department of Biology of Texas A&M University and the Biocenter, Chair Physiologische Chemie, University of Würzburg. MG-O was supported by a  Heep  fellowship. HP acknowledges financial support from the  France Génomique National  infrastructure, funded as part of  Investissement d'Avenir program  managed by  Agence Nationale de la Recherche  (contract  ANR-10-INBS-09 ). NSF Project Grant  IOS-0920088  to  M. J. O'Neill  and  R. J. O'Neill ,  NSF-MRI 0821466  to  Linda Strausbaugh  and  R. J. O'Neill , and  NSF-MRI-R2 0959365  to  M. J. O'Neill  and  R. J. O'Neill. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
DATA AVAILABILITY STATEMENT The genomic and transcriptomic raw reads have been deposited at NCBI under BioProject PRJNA532900, BioSamples SAMN11418710, SAMN11418711. The genome assembly has been deposited at NCBI under Accession No. SZYC00000000. The annotation files have been deposited at GitHub (https:// www. github. com/marianamateos/Poeciliopsis-occidentalisdraft-genome-version1). The assembled and annotated mitochondrial genomes are available under NCBI Acc. Nos. MK860197 and MK860198.
Several tools have been published performing variant annotation, including Annovar, VEP, or SNPeff, which augment the sequencing information with a comprehensive set of current omics, population genomics, and clinical knowledge  (Cingolani et al. , 2012; McLaren et al. , 2016; Wang, Li, & Hakonarson, 2010). These tools utilize a large selection of available databases containing gene annotations, various genomic features, variant allele frequencies in different populations, functional impact prediction, and evolutionary conservation  (Bao et al. , 2014). Other methods, such as eXtasy  (Sifrim et al. , 2013) , PhenoDB  (Sobreira, Schiettecatte, Boehm, Valle, & Hamosh, 2015) , Phen-Gen  (Javed, Agrawal, & Ng, 2014) , VarSifter  (Teer, Green, Mullikin, & Biesecker, 2012) , KGGseq (M. -X. Li, Gui, Kwan, Bao, & Sham, 2012) , and SPRING  (Wu, Li, & Jiang, 2014) , focus on prioritization of potentially causal variants using both functional annotation and clinical information.
These tools systematically filter, evaluate, and prioritize thousands of variants, taking into account knowledge found in genome annotation databases  (Rhead et al. , 2010) , disease gene repositories (OMIM, Online Mendelian Inheritance in Man;  Landrum et al. , 2014) , and patient pedigree information, as well as phenotype descriptions and disease definitions provided for example, as Human Phenotype Ontology (HPO) terms  (Köhler et al. , 2014). Finally, methods such as Endeavour  (Tranchevent et al. , 2008)  and GeneDistiller  (Seelow et al. , 2008)  prioritize disease genes, not individual variants, by integrating diverse genomic data sources. Detection rates of causal variants using WES have been reported to be as low as 20-30% of cases (H. Lee et al. , 2014; Yang et al. , 2013) , although higher success rates have been reported for specific disease or inheritance types  (Sawyer et al.
, 2016) , using gradient-boosting trees on pathogenicity scores and conservation features, and Revel  (Ioannidis et al. , 2016) , using an RF to integrate several pathogenicity predictors. To combine an intuitive user interface with comprehensive variant prediction, annotation, pathogenicity classification, and causal variant prioritization we developed eDiVA (exome Disease Variant Analysis), http://www. ediva. crg. eu. The eDiVA pipeline is composed of four main components: (a) eDiVA-Predict, where sequencing results are processed to predict the presence of genomic variants; (b) eDiVA-Annotate, that enriches variants via a domain-knowledge database; (c) eDiVA-Score, which estimates variant pathogenicity using a random forest model; and (d) eDiVA-Prioritize, in which variants from small groups of related samples (i.
eDiVA has been optimized for two common clinical diagnostics scenarios, parent-child trios comprised of healthy parents and one affected child (tested for recessive, compound heterozygous, and Xlinked inheritance or dominant de novo variants) and families with multiple affected relatives (additionally tested for dominant inheritance). We demonstrate that eDiVA outperforms competing approaches in a semisynthetic benchmark study introducing thousands of known disease variants from ClinVar  (Landrum et al. , 2014)  or HGMD  (Stenson et al. , 2017)  into real WES data from the 1000 Genomes Project CEPH parent-offspring trio of European ancestry (NA12878, NA12891, and NA12892). We, furthermore, report summary statistics on eDiVA and Phen-Gen results for 35 unreported disease cases, composed of 15 cases of spinocerebellar ataxia, 16 cases of primary immunodeficiency, and four cases of congenital myasthenia.
| eDiVA pipeline eDiVA consists of a Python pipeline combined with an SQL Database back-end composed of four components: variant prediction, variant annotation, pathogenicity estimation, and variant prioritization (Figure  S1 ). The main functionality of eDiVA is to process nextgeneration sequencing (NGS) data for small sets of samples (e. g. , families or parent-child trios) and to output a shortlist of potentially causal variants for the diagnosed disease. eDiVA is available as an opensource repository, https://github. com/mbosio85/ediva, with a Docker container composition wrapped within a NextFlow  (Di Tommaso et al. , 2017)  interface to guarantee exact reproducibility on the most common computing platforms (including several cloud platforms) and as a freely accessible web server: http://www.
| eDiVA-Predict: WES or WGS processing and variant calling The eDiVA-Predict module performs sample-wise variant calling according to the recent GATK  (McKenna et al. , 2010)  best practices (https://www. broadinstitute. org/gatk/guide/best-practices as of June 2017) to extract genetic variants from raw reads. Reads in fastq format are aligned using bwa-mem (H. Li, 2013) , alignments are postprocessed using samtools (H. Li, 2011 ), GATK (McKenna et al. , 2010) , Picard (Picard Tools-By Broad Institute), and custom quality filters (details provided in Supporting Information Material). Finally, VCF files are generated using GATK HaplotypeCaller. Subsequent regenotyping of all positions harboring a single-nucleotide variant (SNV) or small insertions and deletions in at least one family member yields a complete matrix of variants for the whole sample set (family) in multisample VCF format.
| eDiVA-Annotate: Functional variant annotation Using the eDiVA-Annotate module each variant is individually linked with public information sources to integrate multiple knowledge domains, and to provide a comprehensive annotation profile. First, ANNOVAR  (Wang et al. , 2010)  is applied to relate each variant to its corresponding gene (choosing among UCSC, Ensembl, or Refseq gene annotations), and to its functional consequence at the protein level. Next, functional, population genomics, and evolutionary data relevant for variant prioritization are added to each variant. To this end we created a MySQL database, eDiVA-DB, containing all relevant positional information obtained from UCSC table browser  (Rhead et al. , 2010)  and other sources. Each variant is annotated with population allele frequency information from the dbSNP  (Sherry et al.