dataesr/scientific-paragraphs-categorization huggingface dataset:
com/linetools 3 https://github. com/pyigm or detected metal line absorption at a given redshift. This results in a sample of strong absorption systems, encompassing primarily LLSs and DLAs, all of which show associated C iv absorption features. The SDSS J074749. 17+115352. 4 sightline has a high incidence of metal and strong Lyα absorption system lines in the redshift range 4 < z < z qso , which we list in Table  3  (note that below z ∼ 4, C iv begins to fall in the Lyα forest for our quasar sample, whilst z ∼ 4 Lyα is obscured by z ∼ 5 Lyβ absorption). The strongest of these is a clear DLA at z ≈ 5. 14. All but one of the other metal-line detected systems lie at lower redshifts than the DLA, meaning that for these we have no measurement of their Lyman limit strengths, or in general any useful measurement of the Lyman series beyond that of Lyβ.
SIMULATION DATA To provide additional context to our observations, we incorporate predictions for absorption line column densities from the eagle suite of cosmological hydro-dynamical ΛCDM simulations  (Crain et al. 2015; Schaye et al. 2015). The simulations were run with a modified version of the smoothed particle hydrodynamics (SPH) code GADGET3, incorporating state-of-the-art numerical techniques and subgrid models used to capture physical processes important to galaxy formation and evolution. These include radiative gas cooling, star formation, mass loss from stars, metal enrichment, en-  ergy feedback from star formation and AGN and gas accretion onto, and mergers of, super-massive black holes. The efficiency of stellar feedback and the mass accretion onto black holes is calibrated to match the present-day stellar mass function of galaxies (subject to the additional constraint that the galaxy sizes need to be realistic), and the efficiency of AGN feedback is calibrated to match the observed relation between stellar mass and black hole mass.
ACKNOWLEDGEMENTS Based on observations made with  ESO Telescopes at the La Silla Paranal Observatory  under programme  ID 0102. A-0261. RMB and MF acknowledge support by the  Science and Technology Facilities Council  [grant number  ST/P000541/1 ]. This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No. 757535 ). MR acknowledges support by  Space Telescope Science Institute 's Director's  Research Funds. SC gratefully acknowledges support from  Swiss National Science Foundation  grant  PP00P2-163824. The Cosmic Dawn center is funded by the  Danish National Research Foundation (DNRF). SL was funded by project  FONDE-CYT 1191232. For access to the codes and advanced data products used in this work, please contact the authors or visit http://www.
Sequence variants were inferred using DADA2  (Callahan et al. 2016b). Chimeras were subsequently removed from the dataset. Taxonomy was assigned with a naïve Bayesian classifier using the Greengenes 13. 8 training set. OTU's with no taxonomic assignment at phylum level or which were assigned as mitochondria were subsequently removed from the dataset. For some taxa of particular interest an additional taxonomic assignment was done by performing a nucleotide BLAST against the NCBI 16S ribosomal RNA database. After filtering, a total of 4642 557 reads were obtained, with a mean of 38 688 reads per sample and most samples having more than 10 000 reads (only two exceptions with 758 and 2821 reads). A neighbor joining phylogenetic tree was constructed and was used as a starting point for fitting a GTR+G+I maximum likelihood tree.
To test for differences in species richness in the bacterioplankton dependent on inoculation treatment, pairwise comparisons were performed using a one-way ANOVA followed by a post-hoc Tukey's HSD test. Correlation between species richness in the bacterioplankton and the gut bacterial community was determined using a linear regression. The effects of inoculation source, host genotype and their interaction on the composition of gut bacterial communities were analyzed by permutational ANOVA  (Anderson 2001 ) on both quantitative (Bray-Curtis dissimilarity and weighted unifrac) and qualitative (Jaccard and unweighted unifrac) distance matrices with 9999 permutations using the adonis function of the R package vegan v2. 5  (Oksanen et al. 2019). To assess the relative contribution of the inoculum and host genotype on the gut bacterial community assembly we used a variation partitioning approach  (Peres-Neto et al.
2006 ). Variation partitioning was performed for both Hellinger transformed counts and presenceabsence data of the gut bacterial communities including both inoculation source and host genotype as variables (varpart function in vegan). Significance of each variable was assessed using the rda and anova. cca functions. In addition to testing differences in β-diversity between treatments on all inoculation treatments simultaneously, we did a separate analysis including only treatments inoculated with natural bacterioplankton communities. We did this because the importance of the bacterioplankton in shaping the gut bacterial community composition could be overestimated by including the artificial hay extract treatment in which the inoculum had a highly divergent composition compared to natural habitats.
The effect of host genotype on the gut bacterial community composition was also additionally assessed for each inoculation treatment separately, as variation in gut bacterial community composition caused by the inoculum could potentially mask more subtle effects of the host genotype. To identify bacterial taxa that have the largest contribution to the differentiation of the gut bacterial community composition according to inoculation source or host genotype, we performed a random forest analysis using OTU's as predictors and inoculation source or host genotype as response variable using the randomForest package  (Liaw and Wiener 2002)  with 100 classification trees. Differential abundances of bacterial taxa in the Daphnia gut between different inoculation sources or host genotypes were tested using DeSeq2  (Love, Huber and Anders 2014).
(12. 6 ± 10. 9%, 16. 6 ± 8. 2% and 16. 3 ± 14. 1%, respectively). In samples inoculated with Daphnia pond bacterioplankton, Comamonadaceae sp. (100% identity to Limnohabitans sp. in NCBI database) and Alcaligenaceae sp. commonly made up a large proportion of the gut bacterial community (6. 6 ± 8. 5% and 4. 7 ± 4. 9%, respectively), while these were less common in other inoculation treatments. Several generally rare taxa also reached a high abundance in single samples: one sample contained 45. 9% Flectobacillus sp. , two samples contained >20% Streptophyta sp. and in one sample three members of the  Acidobacterial order iii1-15 compromised together 41. 6% of the community. Gut bacterial communities in treatments inoculated with fish pond bacterioplankton had a noticeable higher abundance of Polynucleobacter sp.
PHYLOGENY Extraction of genomic DNA and PCR amplification of the 16S rRNA gene of RB5 T was performed as described previously  [11]. Furthermore, the sequence for the 16S rRNA gene of RB5 T was extracted from whole genome data using Artemis  [12]  and the near-complete 16S rRNA gene sequence was used for phylogenetic analyses. blastn analysis was performed using the NCBI database. The results indicated that strain RB5 T is a member of the genus Streptomyces. The 16S rRNA gene sequences of published Streptomyces reference strains were downloaded from the NCBI 'refseq' database  [13]  and pairwise sequence similarities were calculated using the method recommended by Meier-Kolthoff et al. [14]  on the GGDC web server  [15]. sina sequence alignment was used for 16S rRNA gene sequence alignments  [16].
Phylogenetic trees were reconstructed with mega software (version 7. 0. 26) [17]  using the neighbour-joining  [18]  and maximum-likelihood algorithms  [19]. The evolutionary distance model of Tamura  [20]  was used to generate evolutionary distance matrices for the maximum-likelihood and neighbour-joining algorithms with deletion of complete gaps and missing data. For the maximum-likelihood algorithm, discrete gamma distribution was used (+G) and the rate variation model allowed for some sites to be evolutionarily invariable (+I). For the neighbour-joining algorithm, rate variation among sites was modelled with a gamma distribution. The confidence values of nodes were evaluated by bootstrap analysis based on 1000 re-samplings  [21]. Strain RB5 T showed highest 16S rRNA similarity to Streptomyces polyrhachis DSM 42102 T (=NEAU-ycm1 T ; 98.
GENOME FEATURES Genome sequencing of RB5 T was performed as previously described and the G+C content of the DNA was determined from whole sequence data  [11]. DNA-DNA hybridization (DDH) was performed by the German Collection of Microorganisms and Cell Cultures GmbH (DSMZ) Identification service as a classical wet-lab experiment. The required DNA was obtained as described by Cashion et al. [22]  and DDH experiments were performed in duplicate according to the methods of De Ley et al. [23]  and Huss et al. [24]. The wet-lab DDH value of RB5 T and the closest relative S. polyrhachis DSM 42102 T was 27. 4 % (26. 8 %). This value is below the threshold value of 70 % for the delineation of bacterial species recommended by Meier-Kolthoff et al. [14]  and Wayne et al. [25]. The genome of RB5 T was 7.
The type strain, RB5 T (=VKM Ac-2839 T =NRRL B-65539 T ), was isolated from the gut of the termite Macrotermes natalensis (major worker). The GenBank/EMBL accession number for the partial 16S rRNA gene sequence of strain RB5 T is KY558696. 2. This Whole Genome Shotgun project has been deposited at DDBJ/ENA/GenBank under the accession WEGJ00000000 (Bio Project PRJNA545686,; Bio Sample SAMN11898449). The version described in this paper is WEGJ01000000.
Funding information J. S and R. B. were funded by the International Leibniz Research School for Microbial and Biomolecular Interactions (ILRS). Furthermore, R. B. was funded by the Jena School for Microbial Communication (JSMC, DFG). Financial support from the Boehringer Ingelheim Foundation, the Daimler Benz foundation, the Deutsche Forschungsgemeinschaft (DFG, German Science Foundation; (BE 4799/3-1, SFB 1127/2 ChemBioSys -239748522 and BE-4799/3-1) to C. B. and the Villum Kann Rasmussen foundation for a Young Investigator Fellowship (VKR10101) to M. P. is greatly acknowledged.
The Man-Kendall test highlighted a significant upward trend in fire foci for the Atlantic Forest, Cerrado, Pantanal and Pampa biomes. The Pettitt test identified turning points in the fire foci patterns for the Atlantic Forest, Cerrado and Pampa biomes in 2009, 2009 and 2011, respectively. To illustrate this result, we mapped the spatial distribution of fires for 2009 (Fig. 2 ), 2011 (Fig. 3 ), and 2018 using the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor (Fig. 4 ). Note that the 2018 dataset was used to demonstrate a more recent situation). The cluster analysis in Fig. 5 , shows the formation of two homogeneous groups; the first containing the Amazon and Cerrado biomes, and the second containing the Atlantic Forest, Caatinga, Pantanal and Pampa biomes.
According to the Köppen climate classification and the update proposed by Alvares et al. 50 , the climate is tropical with dry winter (Aw). Historical time series of monthly fire foci and carbon emissions. The monthly data on fire foci and carbon emissions was acquired between 1999 and 2018. Fire foci data were calculated using the MODIS sensor product MCD14DL (TERRA/AQUA). Near real-time (NRT) MODIS thermal anomalies/fire locations-collection 6 was processed by the National Aeronautics and Space Administration's Land, Atmosphere Near real-time Capability for EOS (LANCE) Fire Information for Resource Management System (FIRMS), using swath products (MOD14/MYD14) rather than the tiled MOD14A1 and MYD14A1 products. The thermal anomalies/active fire represents the center of a 1 km pixel flagged by the MODIS MOD14/MYD14 fire and thermal anomalies algorithm  51  to contain one or more fires within the pixel.
This characteristic is the most basic fire product in which active fires and other thermal anomalies, such as volcanoes, are identified. Data were downloaded directly from FIRMS (https ://firms. modap s. eosdi s. nasa. gov/) and arranged in the shapefile format (https ://earth data. nasa. gov/activ e-fire-data). The FIRMS fire map enables interactive browsing of the full archive of global active fire detections from MODIS and Visible Infrared Imaging Radiometer Suite (VIIRS). Near real-time fire data was available within approximately 3 h of the satellite overpass and imagery was available within 4-5 h. The same data generated from Terra/Aqua for fire foci was used from the Brazilian fires database  6. Carbon emissions (in Tg) were extracted from the fourth version of the Global Fire Emissions Database  52.
This product provides carbon emissions at a 0. 25° spatial resolution and categorizes emissions by the types of fire to calculate gas traces using emission factors (https ://www. globa lfire data. org/). All of these datasets are based on burned areas driven by a small burned area. Emissions were computed at a monthly temporal resolution and released at this time, as well as a day cycle based on Mu et al. 53. Emission groups contained a 12-month dataset (01, 02,…, 12), with calculated carbon in units of g cm -2 month -1. Biosphere flows included monthly net primary production (NPP), heterotrophic respiration (Rh), and fire emissions (BB), all expressed in g Tg m -2 month -1. ARIMA modeling (past and future) of fire, carbon and CO 2 emissions. The ARIMA model was used to predict expected probable carbon emissions (in Tg) and the number of fire foci for 2019-2030 was based on a number of changes from the 1999-2018 data time series, representing carbon emissions (Tg) and fire foci (number).
Validation took place with the generation of the 10-year dataset; that is, from the model the previous series of 140 samples were generated to predict the variables for the following year. All years considered were validated from statistical indicators, such as the Standard Error of Estimation (EPE), coefficient of determination (R 2 ), and Willmott agreement index (D). The simulation of projected monthly carbon emissions and fire foci variables was conducted for the 12-year period from January 2019 to December 2030 (total 144 samples), using monthly data from 1999 to 2018. According to Sanderson and Fisher 56 , this type of simulation is essential, as it is able to inform future risks and the events of climate change, such as fire and emissions. (1) Y t=c+∅ 1 y d t-1+∅ p y d t-p+•••+∅ 1 e t-1 +∅ q e t-q +e t To compare our results with the Paris agreement regarding the observations conducted on CO 2 emissions in 2005 (2.
1 Gt CO 2 ) and the target set in the Paris agreement by 2030 (1. 2 Gt CO 2 ), we transformed carbon emissions into CO 2 emissions. However, it is already recognized that most carbon emissions occur largely in the form of CO 2. Carbon emission data was converted into CO 2 mass, assuming that any carbon emitted into the atmosphere would oxidize into CO 2. Data was converted from "g of C" to "g of CO 2 ". For this purpose, we used the ratio between the molecular atomic mass of a CO 2 molecule (44) and the mass of a carbon atom (12) (i. e. , 44/12), obtaining a ratio of approximately 3. 7  [57] [58] [59]. All processing was conducted with the R software version 3. 5. 1, using the available libraries (MASS, tseries, forecast, readxl, raster, rgdal, maptools, RSAGA and ggplot2)  60.
In the grouping of biomes, the complete linkage hierarchical method was used (i. e. , complete linkage) as described by Johnson and Wichern 67 and Mardia et al. 68. The agglomerative method of complete connection consists of the measure of similarity between two clusters, defined by the most significant distance from any point in the 1st cluster to any point in the 2nd cluster. All analyses in this study were carried out using R version 3. 4. 3 software 60 , utilizing the following packages; ggplot2, trend, Kendall and factoExtra.
Acknowledgements The authors would like to thank the BDqueimadas of the  National Institute for Space Research (INPE) , the  Fire Information for Resource Management System (FIRMS/NASA ), and the Global Fire Emissions Database (GEFD). This study was financed in part by the  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior-Brasil (CAPES)-Finance  Code  001 ,  National Council for Research and Development (CNPq) , and  Federal University of Mato Grosso do Sul (UFMS). We are also thank to the  research laboratories of State University of Mato Grosso (UNEMAT) ,  Federal Rural University of Rio de Janeiro (UFRRJ) ,  Federal University of Mato Grosso (UFMT) , and Université Rennes 2.
This paper presents isolated photon-hadron correlations using pp and p-Pb data collected by the ALICE detector at the LHC. For photons with |η| < 0. 67 and 12 < p T < 40 GeV/c, the associated yield of charged particles in the range |η| < 0. 80 and 0. 5 < p T < 10 GeV/c is presented. These momenta are much lower than previous measurements at the LHC. No significant difference between pp and p-Pb is observed, with PYTHIA 8. 2 describing both data sets within uncertainties. This measurement constrains nuclear effects on the parton fragmentation in p-Pb collisions, and provides a benchmark for future studies of Pb-Pb collisions.
The measurement of the transverse momentum of γ iso constrains the recoiling parton kinematics in a way that is not possible with inclusive jet production and provides an effective way to probe the nuclear modification of the fragmentation function. Moreover, the per-trigger yield is the ratio of a semi-inclusive cross-section (photon + jet) and inclusive cross-section (photon). Both quantities are sensitive to the nuclear parton distribution functions (PDF) in the same way  [14, 15]. Thus, by measuring per-photon quantities, sensitivity to the nuclear PDF is eliminated. This paper is organized as follows: Section 2 covers the experimental setup; the datasets and simulations are presented in Section 3; isolated photon and charged hadron reconstructions are detailed in Sections 4 and 5; the purity measurement is reported in Section 6; Section 7 describes the correlation measurements; Section 8 reports the systematic uncertainties of the measurement; Section 9 presents the results; and the conclusions are discussed in Section 10.
Datasets The data used for this analysis were collected during the 2013 p-Pb run and the 2017 pp run, both at a center-of-mass energy of √ s NN = 5. 02 TeV. Photon events were selected by the ALICE EMCal trigger. The EMCal issues triggers at two different levels, Level 0 (L0) and Level 1 (L1). The events that pass L0 selection are further processed at L1. The L0 decision, issued at most 1. 2 µs after the collision, is based on the analog charge sum of 4 × 4 adjacent cells evaluated with a sliding window algorithm within each physical Trigger Region Unit (TRU) spanning 8 × 48 cells in coincidence with a minimum bias trigger. The L1 trigger decision, which must be taken within 6. 2 µs after the collision, can incorporate additional information from different TRUs, as well as other triggers or detectors.
Tracking performance The data taking approach during part of the 2017 pp run was to read out only a subset of the ALICE detector systems. This enhanced the sampled luminosity by reading out at a higher rate. This lightweight readout approach included the EMCal and the ITS but excluded the Time Projection Chamber. As a result, ITS-only tracking is used for both pp and p-Pb data in this measurement. This approach differs from the standard ALICE tracking, but it has also been used for dedicated analyses of low momentum particles that do not reach the TPC  [25]. Previous studies using standalone ITS tracking used a maximum track p T of 0. 8 GeV/c  [26]. What is novel in this analysis is the use of an extended range of p T in the ITS-only tracking from 0. 5 to 10 GeV/c. All tracks are required to fulfill the following conditions: at least 4 hits in the ITS detector, a distance of closest approach to the primary vertex in the transverse plane less than 2.
For tracks in pp, the fake rate is 2. 6% at 0. 5 GeV/c and grows linearly to 18% at 10 GeV/c. The following check on the simulation was performed to ensure that it reproduces minimum-bias data. As the yield of charged particles in minimum-bias data is generally independent of ϕ, any dips in the ϕ distribution are clearly visible in both simulation and data. After efficiency corrections, the ϕ distribution is flat within ± 2. 5%. ϕ and η detector-dependent effects on the cluster-track pair acceptance are corrected with the event mixing technique described in Section 7. To validate the combined effect of tracking efficiency, fake rate, and track momentum smearing corrections obtained from simulation of ITS-only tracking, the published charged-particle spectrum in p-Pb collisions at √ s NN = 5.
Purity measurement The purity of the γ iso candidate sample is measured using a two-component template fit. The σ 2 long distribution for the isolated cluster sample is fit with a linear combination of the signal distribution, determined from a photon-jet simulation, and the background distribution, determined from data using an anti-isolated sideband (5. 0 < p iso T < 10. 0 GeV/c) and corrected using a dijet simulation. The MINUIT  [33]  package is used for χ 2 minimization and the MIGRAD package for uncertainty estimation. The only free parameter in the fit is the number of signal clusters, N sig , because the overall normalization, N, is fixed to the total number of isolated clusters: N observed (σ 2 long ) = N sig × S(σ 2 long ) + (N -N sig ) × B(σ 2 long ), (3) where S(σ 2 long ) and B(σ 2 long ) are the normalized signal and background templates.
The ratio of the shower-shape distributions of clusters in the signal (Iso, p iso T < 1. 5 GeV/c) region and sideband (Anti-iso, 5. 0 < p iso T < 10. 0 GeV/c) region is constructed via R(σ 2 long ) = Iso MC (σ 2 long ) Anti-iso MC (σ 2 long ). (4) This ratio of shower shape distributions is applied as a multiplicative correction to the background template: B corr. (σ 2 long ) = Anti-iso data (σ 2 long ) × R(σ 2 long ). (5) This background template correction results in an absolute correction on the purity of 8%-14% depending on the cluster p T. The purities as a function of the cluster p T are shown in Figure  2. They are compatible between the pp and p-Pb datasets within the uncertainties. A three-parameter error function is fit to the data. The fits have been checked with several bin variations to ensure that they accurately represent the quickly rising purity at low p T.
The uncertainty in the purity measurement is propagated to the correlation function measurement following Eq. 10. The resulting uncertainty on the correlation function is ±18% for pp data and ±11% for p-Pb data. A large fraction of the total uncertainty in the purity is either statistical uncertainty or systematic uncertainties that arise due to limited data sample. Therefore, uncertainties arising from the purity in the pp and p-Pb data are largely uncorrelated in the γ-hadron analysis. To be conservative, they are taken to be totally uncorrelated. The uncertainty on the purity in pp is larger than in p-Pb due to the pp dataset having lower statistics: the background templates are directly obtained from data, and the uncertainty on the signal template is evaluated using data as well.
Underlying Event Subtraction The uncertainty in the underlying event subtraction originates from statistical fluctuations in the ZYAM estimate and propagates directly to the per-trigger hadron yields. This uncertainty ranges from 7% to 15% depending on the z T bin and data set. The uncertainty is fully correlated in ∆ϕ for a given z T bin, but totally uncorrelated among z T bins. It is also uncorrelated between the pp and p-Pb datasets.