dataesr/scientific-paragraphs-categorization huggingface dataset:
Metrics used to evaluate modelled burned area against GFED3. 1 data As the GFED3. 1 data is most widely used by the fire modelling community, the model results are evaluated against GFED3. 1 data for 1997-2009. Three aspects were examined: mean annual burned area, interannual variability (IAV) and seasonality in burned area. The evaluation was done for each GFED3. 1 region (Fig. 2 ). For the model error in terms of mean annual burned area (BA), we use the relative difference: E BA = BA model -BA GFED BA GFED (2) where BA model is the simulated burned area averaged over 1997-2009, and BA GFED is GFED3. 1 mean annual burned area for the same period. The similarity in IAV (S interannual ) is estimated by the correlation coefficient of the two linearly detrended annual burned area time series by model and GFED3.
1 data. Finally, the seasonality similarity (S season ) is given by: S season = 12 i=1 min(frac_model i , frac_GFED i ) (3) where frac_model i and frac_GFED i are the fraction of burned area for the ith month relative to the annual burned area (i. e. monthly BA normalised by the annual BA). S season represents the overlapping area of the two normalised monthly BA series and indicates the fraction of burned area in temporal coincidence. The statistical significance of S season was examined by using a bootstrapping method. First, normalised monthly BA from all 14 regions by the model and GFED3. 1 data were pooled together. Second, 100 000 pairs of monthly normalised BA were randomly sampled from the pooled data in order to derive a probability distribution function (PDF) of S season. Third, the single-sided probability (p value) that the calculated S season is from random distribution is obtained for each region, and a p value less than 0.
ORC-err-burn, where ORCHIDEE shows burning but the other three observation data sets do not; ORC-err-noburn, where at least two of the three observation data sets do show burning, but ORCHIDEE does not; ORC-min, where ORCHIDEE simulates lower burned fraction than the other three data sets; ORC-max, where ORCHIDEE simulates higher burned fraction; ORC-good, where ORCHIDEE-simulated burned fraction falls within the range given by the three observation data sets. When calculating the minimum and maximum burned fraction of the observation data sets, an arbitrary tolerance margin of 25 % was applied around the min/max value to take into account the observation uncertainty.
Comparison of model simulation to satellite observation for the spatial and temporal pattern of burning Figure  4  shows the spatial distribution of mean annual burned fraction by the model and the three satellite-derived data sets (GFED3. 1, GLOBCARBON, L3JRC) for 2001-2006. L3JRC and GLOBCARBON show similar spatial patterns of burning, which is different from the GFED3. 1 data. Generally, L3JRC and GLOBCARBON have less burned area in the Southern Hemisphere than GFED3. 1 (see also Fig. 5a ), with smaller spatial extent of burning in the savanna systems in Africa and Australia. By contrast, in the middle to high latitudes of the Northern Hemisphere, L3JRC and GLOB-CARBON show more burned area than GFED3. 1. All three data sets capture the grassland burning in central and eastern Asia. ORCHIDEE coupled with SPITFIRE is generally able to reproduce the spatial distribution and magnitude of satellite-observed burned fraction.
The simulated mean annual global burned area for 2001-2006 is 346 Mha yr -1 , which falls within the range of 287-384 Mha yr -1 given by the satellite observation data, and close to the 344 Mha yr -1 by the GFED3. 1 data set when crop fires are excluded. Fires in grassland-dominated systems are well captured by the model, including steppe fires in central and eastern Asia, savanna fires in northern Africa, northern Australia and central to east South America. Two regions could be identified where model simulation is different from all the three observation data sets. One is the woodland savanna (miombo) in southern Africa, where burned area is underestimated by the model (simulated annual burned fraction is ∼ 4 %, but 14-24 % is observed). The other is western and central continental US (dominated by C3 and C4 grass in the land-cover map used by ORCHIDEE) where fires are overestimated (simulated annual fraction is ∼ 6%, but 1-2 % is observed).
General model performance The SPITFIRE module was first presented by  Thonicke et al. (2010). It was later adapted in the Land surface Processes and exchanges (LPX) model by  Prentice et al. (2011) , notably with the removal of ignition sources created by humans, and further by  Pfeiffer et al. (2013)  and  Lasslop et al. (2014). Pfeiffer et al. (2013)  developed a scheme to allow fire span of multiple days and fire coalescence, explored the role of lightning interannual variability in the model, and included terrain effects on fire at a broad scale. Lasslop et al. (2014)  investigated model sensitivity to climate forcing and to the spatial variability of a number of fire relevant parameters. In the current study, the SPITFIRE module was fully integrated into the global process-based vegetation model ORCHIDEE for the first time.
Acknowledgements. The authors thank  Nicolas Vuichard ,  Arnaud Caubel , and  Josefine Ghattas  for their technical help. We thank  John Little  from  Canadian Forest Service  on the help to interpret Canadian fire data. We thank  Jed. O. Kaplan ,  Mirjam Pfeiffer ,  Allan Spessa , and the two anonymous referees for their extensive review and discussion comments, which improved this manuscript. Funding for this work was provided by the  ESA  firecci project (http://www. esa-fire-cci. org/) and  EU   FP7  project  PAGE21. Edited by:  D. Lawrence.
Results and Discussion We extended the sequencing by Schaschl et al. [27]  and Alasaad et al. [28]  from 125 to 203 Alpine ibex from different populations and did not find additional alleles at the exon 2 of the MHC DRB locus. We found that the second allele (Caib-DRB*2) was identical to the Cahi-DRB*16 allele of the domestic goat first reported from the Japanese breed Shiba (  [29] , Genbank accession AB008361). We will refer to Caib-DRB exon 2 sequence variants (236 bp) as Caib-DRB alleles throughout the manuscript (see Figure  1 ). We found no recombinant between the two alleles in Alpine ibex, although recombinants at this locus were found in several related ungulate species  [27, 30, 31]. This suggests that the two alleles are not both ancient alleles of the Alpine ibex DRB locus. We investigated the geographic distribution of the Caib-DRB alleles by genotyping the microsatellite marker OLADRB1 (Figure  1A ).
Population genetic signature of introgression in the MHC DRB region Introgression is expected to generate blocks of linkage disequilibrium due to the recent integration of sequences from another species. We used population and species level data for the analysis of linkage disequilibria in order to control for potential alternative sources of linkage disequilibrium [reviewed in 44]. We identified nearly complete linkage disequilibrium between Caib-DRB*2 and two proximal microsatellite markers (OLADRB1 and OLADRB2) covering a physical distance of 107 to 161 kb in the cattle and sheep genome, respectively (Figure  1C ). Complete linkage disequilibrium with Caib-DRB*2 was observed for one of four alleles of the diagnostic marker OLADRB1 (allele OLADRB1/ 184; n = 156, Figure  1D  and Table  S4 ).
Allele OLADRB1/184 was nearly completely associated with allele OLADRB2/277 (correspondence between the two markers in 705 out of 707 individuals). Based on data from the 52 K Goat SNP Chip, we calculated linkage disequilibria among SNPs. We found a large block of high linkage disequilibrium containing the MHC DRB in Alpine ibex populations with high Caib-DRB*2 allele frequencies (e. g. population Cape au Moine: r 2 = 0. 85 across 2. 1 Mb, Figure  4A ; Table  S5 ). Two smaller linkage disequilibrium blocks (0. 2 and 0. 4 Mb with r 2 $0. 85) were observed in the Alpine ibex population with the lowest frequency of the Caib-DRB*2 allele (population Weisshorn, Figure  5A  and 6A; Table  S5 ). The smaller blocks of linkage disequilibria in the Weisshorn population may also be explained by a general depletion of genetic diversity at the MHC DRB.
05 (Biomatters, Inc. ). The MHC region of cattle and sheep is homologous to goat chromosome 23 and there is strong colinearity between goat and cattle chromosomes  [40]. We verified the sequence homology using BlastN to search the NCBI Genbank database. The closest hits to our sequences were previously published DRB exon 2 sequences of Alpine ibex and domestic goats. The MHC class II DRB in domestic goats is located on chromosome 23 at 24. 9 Mb and is fully contained on scaffold2167  [40]. Correspondence to BoLA-DRB3 and Ovar-DRB1 was verified by BlastN searching the DRB exon 2 sequence in the cattle and sheep genomes (Figure  1C ).
The forward primer of OLADRB1 overlaps with the primers HL031 and HL032. OLADRB2 (also known as OLADRB, Genbank UniSTS: 251420) has been localized to the BoLA-DRB2 gene in cattle and is located at 107 kb from exon 2 of MHC DRB and OLADRB1 (161 kb in sheep, Figure  1C ). See Table  S6  for primer sequences and references. PCR conditions for OLADRB2 were as described in  [25]. For OLADRB1, we used a reaction volume of 6 ml containing 1. 5 ml (3-30 ng) of DNA template, 0. 4 mM of both forward and reverse primers and 3 ml Qiagen Multiplex PCR Kit. PCR cycling conditions included an initial denaturation step at 95uC for 15 minutes. Microsatellite quality controls and genotyping procedures were followed according to  [25].
8 ml library template. Amplification master mixes were divided into 6 separate 20 ml reactions: 98uC 30 s; 14 cycles {98uC 10 s, 65uC 30 s, 72uC 30 s}; final extension for 59 at 72uC. All purification steps were performed using a MinElute PCR purification kit (Qiagen) according to the manufacturer's recommendations. The library was sequenced on an Illumina HiSeq 2000 platform (100 bp, paired-end). The FASTX-toolkit was used for P1 barcode splitting (http:// hannonlab. cshl. edu/fastx_toolkit/index. html) and Trimmomatic 0. 30 (www. usadellab. org/cms/index. php?page = trimmomatic) was used for adapter and quality trimming. Reads were then aligned to the domestic goat reference genome  [40]  using Bowtie2 2. 1. 0 [52]. Genotypes were called using UnifiedGenotyper (GATK, version 2. 6. 5; [53, 54] ) and filtered using VariantFiltration and SelectVariants (QD,2.
0, MQ,30. 0, 212. 5. MQRank-Sum. 12. 5, FS. 40. 0, HaplotypeScore. 12. 0, ReadPosRank-Sum,28. 000, QUAL. 30. 0, AN. 20). PGDSpider version 2. 0. 3. 0  [55]  was used for data format conversions and to remove genotypes with a genotyping phredscaled quality score lower than 20. PLINK v. 1. 0. 7  [56]  was used for additional genotype filtering. We required a genotyping rate of SNPs. 70% and a minor allele frequency. 0. 01. PLINK was also used to calculate expected heterozygosity. The R package {ggplot2} was used for data visualization.
1 for SNP calling and quality filtering. We used PLINK v. 1. 0. 7  [56]  for further locus filtering. Twelve individuals (including one Alpine ibex) were discarded because we required a genotyping rate of at least 90% per individual. Furthermore, SNPs with a genotyping rate lower than 90% (2,817 SNPs) and a minor allele frequency in Alpine ibex below 0. 01  (49, 832 SNPS)  were removed from the data set. Twenty-one additional SNPs were removed because of an observed heterozygosity of one, which may be due to the presence of a duplicated region. A total of 677 SNPs were retained with a genotyping rate of 98% in the remaining individuals. A minor allele frequency of 0. 05 (570 SNPs) was applied for the linkage disequilibrium analysis, resulting in a genotyping rate of 99. 6%. PGDSpider version 2.
Phylogenetic and population genetic analyses We constructed haplotype networks of phased SNP data (fastPHASE version 1. 4. 0, [57] ) based on the Neighbor-Net algorithm implemented in Splitstree 4. 13. 1  [43]. The networks were based on the uncorrected p distance using concatenated SNP loci. Pairwise linkage disequilibrium (LD; r 2 ) estimates among SNPs were obtained using the R package {genetics}. Heatmaps of pairwise LD estimates were produced using the function LDheatmap in R package {LDheatmap}. In order to investigate the decay of LD over distance, a regression of r 2 values against pairwise distance was plotted (Figure  6 ). The expected values of r 2 were computed using non linear least squares (nls in R) as shown in the following function by  [58]. E r 2 À Á ~10zC 2zC ð Þ 11zC ð Þ ! 1z 3zC ð Þ 12z12CzC 2 À Á n 2zC ð Þ 11zC ð Þ ! Where n is the number of haplotypes, C = r * distance[bp] and r = 4N e r (population recombination rate).
All LD analyses were performed for each Alpine ibex population and domestic goat breed separately. The software STRUCTURE  [39]  was used in order to search for recent hybrids. See figure legends S1 and S2 for details on parameter values. Measures of the extended haplotype homozygosity (EHH,  [46] , Figures 7, S6, S7) were calculated from phased data (fastPHASE version 1. 4. 0, [57] ) using the R package {rehh}  [59]. The same package was used for the bifurcation diagrams. We used physical distances for the EHH analyses and refrained from performing statistical tests, as we did not have access to genotyped Alpine ibex families and, hence, estimates of genetic distances.
Assessing Quelo template-based queries Using the Quelo interface, we generated a set of 41 queries chosen to capture different combinations of concepts and relations. Eight persons (four native speakers of English, four with C2 level of competence for foreign learners of English) were then asked to classify (a binary choice) each query in terms of clarity and fluency. Following  (Kow and Belz, 2012) , we take Fluency to be a single quality criterion intended to capture language quality as distinct from its meaning, i. e. how well a piece of text reads. In contrast, Clarity/ambiguity refers to ease of understanding (Is the sentence easy to understand?). Taking the average of the majority vote, we found that the judges evaluated the queries as non fluent in 50% of the cases and as unclear in 10% of the cases.
In other words, template based queries were found to be disfluent about half of the time and unclear to a lesser extent. The major observation made by most of the participants was that the generated text is too repetitive and lacks aggregation. Comparing template-and grammar-based queries In this second experiment, we asked 10 persons (all proficient in the English language) to compare pairs of NL queries where one query is produced using templates and the other using our grammar-based generation algorithm. The evaluation was done online using the LG-Eval toolkit  (Kow and Belz, 2012)  and geared to collect relative quality judgements using visual analogue scales. After logging in, judges were given a description of the task. The sentence pairs were displayed as shown in Figure  3  with one sentence to the left and the other to the right.
That is, in general, the extracted lexicons permit covering about 85% of the ontological data. In addition, we evaluated the coverage of our approach by running the generator on 40 queries generated from five distinct ontologies. The domains observed are cinema, wines, human abilities, disabilities, and assistive devices, e-commerce on the Web, and a fishery database for observations about an aquatic resource. The extracted lexicons contained in average 453 lexical entries and the coverage (proportion of DL queries for which the generator produced a NL query) was 87%. Fuller coverage could be obtained by manually adding lexical entries, or by developing new ways of inducing lexical entries from ontologies (c. f. e. g. (Walter et al. , 2013) ).
We aim to sensitize the scientific community and the European authorities for the foreseen risks from radioactivity redistribution over Europe. The global model LMDZORINCA that reads deposition density of radionuclides and burnt area from satellites was used, whereas risks for the human and animal population were calculated using the Linear No-Threshold (LNT) model and the computerized software ERICA Tool, respectively. Depending on the scenario, whereas between 20 and 240 humans may suffer from solid cancers, of which 10-170 may be fatal. ERICA predicts insignificant changes in animal populations from the fires, whereas the already extreme radioactivity background plays a major role in their living quality. The resulting releases of 137 Cs after hypothetical wildfires in Chernobyl's forests are classified as high in the International Nuclear Events Scale (INES).
In addition, an ingestion contribution of 7% was assumed given the recent experience in Fukushima  (Evangeliou et al. , 2013b). For the respective doses after the Chernobyl accident only deposition was taken into consideration. Since the Chernobyl accident, a great challenge in radioecology has been the development of a tool that will be able to record and assess the changes in the population of non-human biota due to effects from ionizing radiation. For this reason, a supporting computerbased software (the ERICA Tool) has been developed  (Brown et al. , 2003; Larsson, 2004) , which guides the user through the assessment process, recording information and decisions and allowing the necessary calculations to estimate risks to selected animals and plants  (Larsson, 2008). The results can be put into context using incorporated databases on dose effects, relationships and background dose rates.
Belarus and Russia. Soil concentrations were calculated from deposition densities provided by our model (Bq m -2 ) applying a conversion factor of 228 kg m -2 based on 542 measurements of soil concentration and deposition density in the CEZ and Belarus  (Kashparov et al. , 2003). Soil concentrations calculated for the three fire scenarios were added to the background soil average soil concentration estimated by the same database  (Kashparov et al. , 2003)  (Fig. S4 ). Hence, 5-day average dose rates (μGy h -1 ) were calculated for the reference organisms exposed to this environment. For comparison, the doses from the Chernobyl and Fukushima accidents were also calculated (Fig. 6 ) for the same organisms that are assumed to live in the contaminated forests, as well as in the FEZ (Fukushima Exclusion Zone).
7. The normalized mean bias (NMB) of our model (LMDZORINCA) was estimated to be 65%, in contrast to 42% estimated for  Strode et al. (2012). However, the percentage of sites with deviations of the modeled concentrations from the observations less than a factor of 2 was 56% using our model, whereas the same parameter was 50% as reported in  Strode et al. (2012)  and the correlation coefficient of a linear fitting was slightly improved (0. 45). A Student's T-Test was also performed to compare our results with those of  Strode et al. (2012)  achieving a high probability (P ≈ 1), which shows that the two datasets cannot be distinguished. Small differences can be attributed to the different methodology used to estimate surface activity concentrations of 137 Cs. Strode et al. (2012)  simulated concentrations of 137 Cs by multiplying the modeled POM tracers by the ratio of 137 Cs emission to POM emission from biomass burning (=0.
Regarding MODIS area burnt,  Urbanski et al. (2009)  reported that the uncertainty could rise up to 40% depending on the pixel and time resolution, the fire size etc. In addition, although the deposition density provided by the simulation of the Chernobyl accident  (Evangeliou et al. , 2013a)  has been validated using the REM database  (De Cort et al. , 1998) , there is no unique study on effective half-life variations in Europe that could certify that the value used here (10 years) was realistic and represents all Europe. Moreover, the emission factor of 137 Cs is strongly dependent on the type of the fuel and the prevailing temperature of the flame during a fire. Assuming that the whole pixel is covered by biomass, the emission factor may range from 20 to 100%. Hence, uncertainties can increase by up to more than 50% in our estimations, which are propagated to the health assessment of animal and human populations.
Acknowledgements This study was funded by the  GIS Climat-Environnement-Société  (http://www. gisclimat. fr/projet/radioclimfire). It was also granted access to the HPC resources of [CCRT/TGCC/CINES/IDRIS] under the allocation 2012-t2012012201 made by  GENCI  ( Grand Equipement National de Calcul Intensif ).
Adaptive Covariance Descriptor Another way to reduce variance caused by tiny eigenvalues is to use PCA projection to remove those unreliable dimensions while preserving dominant information in the principal components. Specifically, we first extract raw features from the image patch to form a set of n d-vectors (n indicates the number of pixels in this region and d is the dimension of features); based on this point set, we not only construct the original covariance matrix but also learn a PCA projection. The d-dimensional dataset is then projected to a subspace by the learned PCA projection yielding a compact k-dimensional point set. Finally, the adaptive covariance matrix descriptor is constructed using the projected point set. For a candidate image patch to be compared with the template, the descriptor computation is similar except that it employs the PCA projection pre-learned from the template point set.
Empirical Evaluation In order to validate the effectiveness of the claimed improvements to the conventional region covariance descriptor, we empirically assessed the performances of the regularized descriptor and the adaptive descriptor in comparison with that of the conventional descriptor by repeatedly detecting objects in real-world video sequences. As a benchmark, three publicly available sequences, namely "PkTest01", "PkTest02" and "PkTest03", from the VIVID airborne sensor dataset  [48, 49]  were used for evaluation. The three sequences from VIVID dataset are thermal infrared data of vehicles captured by moving cameras in airport circumstances. These sequences are selected because there is strong correlation between the color channels. Therefore, if many color features are used, there will be some very small eigenvalues in the covariance matrix.
In addition, there are similar vehicles in the scene, making the detection challenging. As the sequences are very long, we used only the first 100 frames of each sequence. In addition, a public color sequence from the PETS dataset [50] is used as well. The sequence is captured from a static camera in a campus circumstance, where we seek to detect a walking pedestrian. There are some other pedestrians in the scene. Similar to the VIVID sequences, we used only 100 frames (from Frame #1412 to Frame #1511) of the sequence. Figure  1  displays the target objects, marked in rectangles in the first frames. Using the annotated template image in the first frame, we first computed the three descriptors, i. e. , the conventional descriptor, the regularized descriptor and the adaptive descriptor of the object respectively.
The first sequence is the "David-outdoor" sequence from  [22] , where the target undergoes partial occlusion, total occlusion, pose change and non-rigid deformation. The second one is from a self-captured video, where two pedestrians enter an outdoor campus environment, occasionally occluded by the background. This sequence contains occlusions, appearance variations and cluttered scenes. We refer to it as the "White-outdoor" sequence because the target pedestrian is in white clothes in the scene. The third sequence is the "Pedestrian1" sequence from the TLD dataset  [57]. This sequence is captured by a moving camera, hence with unpredictable camera moves and large appearance variations. We compared the proposed method with a state-of-the-art tracking method, the "MILTracker" from  [58] , which uses online multiple instance boosting to handle partial occlusion.