dataesr/scientific-paragraphs-categorization huggingface dataset:
ND280 simulation A detailed simulation is used to interpret the data recorded by ND280. The neutrino flux model described in Sec. II A is combined with the NEUT neutrino interaction model described in Sec. III A and a detailed material and geometrical description of the ND280 detector including the magnet, to produce a simulated sample of neutrino interactions distributed throughout the ND280 detector with the beam time structure. For studies of particles originating outside of the ND280 detector, separate samples are produced using a description of the concrete that forms the near detector hall and the surrounding sand. The passage of particles through materials and the ND280 detector response are modeled using the GEANT4 toolkit  [89]. To simulate the scintillator detectors, including the FGDs, we use custom models of the scintillator photon yield, photon propagation including reflections and attenuation, and electronics response and noise  [90].
ND280 detector systematics In this section we explain how we use control samples to assess uncertainty in the modeling of FGD and TPC response and of neutrino interactions outside of the fiducial volume of the FGD. TPC systematic uncertainties are divided into three classes: selection efficiency, momentum resolution and PID. The efficiency systematic uncertainty arises in the modeling of the ionization, cluster finding (where a cluster is defined as a set of contiguous pads in a row or column with charge above threshold), track finding, and charge assignment. This is assessed by looking for missed track components in control samples with particles that pass through  all three TPCs. The single track-finding efficiency is determined to be (99. 8 +0. 2 -0. 4 %) for data and simulation for all angles, momenta and track lengths, and shows no dependence on the number of clusters for tracks with 16 clusters or more.
The inefficiency due to the overlap from a second nearly collinear track is found to be negligible for both data and simulation, so this systematic uncertainty can be ignored. The same control samples are used to evaluate the charge mis-identification systematic uncertainty. This systematic uncertainty is  evaluated by comparing data and simulation of the charge mis-identification probability as a function of momentum. This is found to be less than 1% for momenta less than 5 GeV/c. The momentum resolution is studied using particles crossing at least one FGD and two TPCs by evaluating the effect on the reconstructed momenta when the information from one of the TPCs is removed from the analysis. The inverse momentum resolution is found to be better in simulations than in data, typically by 30%, and this difference is not fully understood.
A scaling of the difference between true and reconstructed inverse momentum is applied to the simulated data to account for this. Uncertainty in the overall magnetic field strength leads to an uncertainty on the momentum scale of 0. 6%, which is confirmed using the range of cosmic ray particles that stop in the FGD. The TPC measurement of energy loss for PID is evaluated by studying high-purity control  samples of electrons, muons and protons. The muon control sample has the highest statistics and is composed of particles from neutrino interactions outside the ND280 detector that pass through the entire tracker. For muons with momenta below 1 GeV/c, the agreement between data and simulation is good, while above 1 GeV/c the resolution is better in simulation than in data. Correction factors are applied to the simulation to take into account this effect.
The performance for track finding in the FGD is studied separately for tracks which are connected to TPC tracks and tracks which are isolated in the FGD. The TPC-FGD matching efficiency is estimated from the fraction of through-going muons, in which the presence of a track in the TPC upstream and downstream of the FGD implies that a track should be seen there. The efficiency is found to be 99. 9% for momentum above 200 MeV/c for both simulation and data. The FGD-only track efficiency is computed as a function of the direction of the track  using a sample of stopping protons going from TPC1 to FGD1. This efficiency is found to be slightly better for data than simulation when cos θ µ < 0. 9. A correction is applied to the simulation to account for this and the correction uncertainty is included in the overall detector uncertainty.
The FGD PID performance is evaluated by comparing the energy deposited along the track with the expected energy deposit for a given particle type and reconstructed range in the FGD. We use control samples of muons and protons tagged by TPC1 and stopping in FGD1. The pull distributions (residual divided by standard error) for specific particle hypotheses (proton, muon or pion) for data and simulation are fitted with Gaussian distributions. To account for the differences in the means and widths of the distributions between data and simulation, corrections are applied to simulation and the correction uncertainty is included in the overall detector uncertainty. The Michel electron tagging efficiency is studied using a sample of cosmic rays that stop in FGD1 for which the delayed electron is detected.
The Michel electron tagging efficiency is found to be (61. 1 ± 1. 9)% for simulation and (58. 6 ± 0. 4)% for data. A correction is applied to simulation and the correction uncertainty is included in the overall detector uncertainty. The uncertainty on the mass of the FGD, computed using the uncertainties in the size and density of the individual components, is 0. 67%  [84]. There is systematic uncertainty in the modeling of pion interactions traveling through the FGD. This is evaluated from differences between external pion interaction data  [40] [41] [42] [43] [44] [45] [46] [47] [48] [49] [50] [51]  and the underlying GEANT4 simulation. The external data do not cover the whole momentum range of T2K, so some extrapolation is necessary. Incorrect modeling can migrate events between the three sub-samples and for some ranges of momentum this produces the largest detector systematic uncertainty.
An analysis of the events originating from neutrino interactions outside the ND280 detector (pit walls and surrounding sand) is performed using a dedicated simulation (sand muon simulation). The data/simulation discrepancy is about 10% and is included as a systematic uncertainty on the predicted number of sand muon events in the CC-inclusive sample. Pileup corrections are applied to account for the inefficiency due to sand muons crossing the tracker volume in coincidence with a FV event. The correction is evaluated for each dataset separately and is always below 1. 3%; the systematic uncertainty arising from this correction is always below 0. 16%. Table XI shows the full list of base detector systematic effects considered and the way each one is treated within the simulated samples to propagate the uncertainty.
NEAR DETECTOR ANALYSIS In this section we explain how we use the large and detailed samples from ND280 in conjunction with models for the beam, neutrino interactions, and the ND280 detector to improve our predictions of the flux at SK and some cross section parameters. The systematic parameters for the beam model ( b), binned in energy as shown in Fig. 1 , the cross section model ( x), listed in Tab. VII, and detector model ( d), illustrated in Fig. 13 , are used to describe the systematic uncertainties in the analysis. We use the three ν µ CC samples described in Sec. IV B and external data discussed in Sec. III B and summarize our knowledge of the neutrino cross section parameters and unoscillated neutrino flux parameters with a covariance matrix, assuming that a multivariate Gaussian is an appropriate description.
Instead, we estimate the four oscillation parameters, |∆m 2 |, sin 2 θ 23 , sin 2 θ 13 , δ CP , and the mass hierarchy, and use external measurements for the solar oscillation parameters, sin 2 θ 12 and ∆m 2  21 , as we have negligible sensitivity to those. Figure  25  illustrates how our key observables depend on the two parameters, sin 2 θ 23 and δ CP , for the two mass hierarchies. In this figure the neutrino energy is at the oscillation maximum (0. 6 GeV), and the other oscillation parameters are fixed (solar parameters as established in Sec. VII B and sin 2 θ 13 = 0. 0243). To a good approximation, with our current dataset, ν µ -disappearance can be treated on its own to estimate θ 23. The oscillation parameter dependence on ν e -appearance cannot be factorized, however. In order to estimate the full set of oscillation parameters and properly account for all uncertainties, it is necessary to do a joint analysis of ν µ -disappearance and ν e -appearance.
32 (∆m 2  13 ) in the case of the normal (inverted) hierarchy. Each successive measurement analyzed a larger dataset, and the most recent measurement provides the world's strongest constraint on sin 2 θ 23  [10]. This section gives a more detailed description of that analysis and the study of multi-nucleon effects. Reducing the uncertainty on the values of these two parameters is important for measuring CP violation in neutrino oscillations by T2K and other current and future experiments. Furthermore, precise measurements of sin 2 θ 23 could constrain models of neutrino mass generation  [107] [108] [109] [110] [111] [112].
B. Determining Confidence Intervals As explained in Sec. VII C, the Neyman method with the approach recommended by Feldman and Cousins (FC) was used to calculate confidence intervals for the two oscillation parameters, sin 2 θ 23 and ∆m 2  32 (∆m 2  13 ), for the normal (inverted) hierarchy. The constant-∆χ 2 method does not provide correct coverage due to the physical boundary near sin 2 2θ 23 = 1 and because of the non-linear parametrization. Critical values of the FC statistic were determined on a fine grid of the two oscillation parameters of interest using 10,000 toy datasets at each point. Each toy dataset had a set of values of the systematic parameters sampled from a multi-dimensional Gaussian having means at the nominal values, and covariances V. Each oscillation parameter, sin 2 θ 12 , ∆m 2  21 , and sin 2 θ 13 , is sampled from a Gaussian with mean and sigma values listed in Sec.
ACKNOWLEDGMENTS We thank the  J-PARC  staff for superb accelerator performance and the  CERN NA61/SHINE collaboration  for providing valuable particle production data. We acknowledge the support of  MEXT, Japan ;  NSERC ,  NRC  and  CFI ,  Canada ;  CEA  and  CNRS/IN2P3 , France;  DFG, Germany ;  INFN, Italy ;  National Science Centre (NCN) , Poland; RSF,  RFBR  and  MES , Russia; MINECO and ERDF funds, Spain;  SNSF  and  SER , Switzerland; STFC, UK; and  DOE, USA. We also thank  CERN  for the UA1/NOMAD magnet,  DESY  for the HERA-B magnet mover system,  NII  for  SINET4 , the  WestGrid and SciNet consortia in Compute Canada, GridPP, UK , and the  Emerald High Performance Computing facility in the Centre for Innovation, UK. In addition participation of individual researchers and institutions has been further supported by funds from:  ERC  ( FP7 ),  EU ;  JSPS, Japan ;  Royal Society, UK ;  DOE   Early Career program ,  USA.
The hypotheses that beta diversity should increase with decreasing latitude and increase with spatial extent of a region have rarely been tested based on a comparative analysis of multiple datasets, and no such study has focused on stream insects. We first assessed how well variability in beta diversity of stream insect metacommunities is predicted by insect group, latitude, spatial extent, altitudinal range, and dataset properties across multiple drainage basins throughout the world. Second, we assessed the relative roles of environmental and spatial factors in driving variation in assemblage composition within each drainage basin. Our analyses were based on a dataset of 95 stream insect metacommunities from 31 drainage basins distributed around the world. We used dissimilaritybased indices to quantify beta diversity for each metacommunity and, subsequently, regressed beta diversity on insect group, latitude, spatial extent, altitudinal range, and dataset properties (e.
2015a ). The relative importance of species sorting versus spatial processes may be contingent on the lengths of environmental gradients (e. g. , range in stream temperature or nutrient concentrations within a drainage basin) and the spatial extent of the study  (Jackson et al. 2001; Cottenie 2005; Bini et al. 2014; Heino et al. 2015a ). One might expect species sorting to increase with increas-ing environmental gradient length  (Jackson et al. 2001; Gr€ onroos et al. 2013 ) and spatial factors to gain importance with increasing spatial extent of the region studied  (Cottenie 2005; Heino 2011 ). Very few studies have explicitly tested these hypotheses using multiple datasets, which is surprising given that environmental heterogeneity and spatial scale are two key ideas behind metacommunity theory  (Leibold et al.
2015b ). These and other hypotheses associated with broad-scale patterns are difficult to test with a single or a few datasets, and more general perspectives may be obtained through a comparative analysis of multiple datasets. Using a large number of stream insect datasets from different parts of the world, we attempted to reveal the main factors structuring stream insect metacommunities by answering the following questions: (Q1) How well can variability in beta diversity across multiple stream insect metacommunities (i. e. , spatial level 1; Appendix S1) be accounted for by insect group, latitude, altitudinal range, and spatial extent? (Q2) What are the relative roles of species sorting and spatial processes in predicting assemblage composition within stream insect metacommunities (i. e. , spatial level 2; Appendix S2)? We assembled a comprehensive dataset comprising 95 stream insect metacommunities from different regions, ranging from tropical to Arctic latitudes and lowland to montane habitats.
Study areas and datasets We assembled data for five major stream insect taxa: mayflies (Ephemeroptera), stoneflies (Plecoptera), caddisflies (Trichoptera), nonbiting midges (Diptera: Chironomidae), and dragonflies (Odonata) from 31 drainage basins distributed around the world, but predominantly from the Americas and Europe (Fig. 1 ). These five insect groups often dominate in stream invertebrate communities Figure  1. Geographical locations of the 31 drainage basins in this study. Analyses were carried out for each insect taxon separately. Thereby, 95 datasets were available for analyses of biological data only and 61 datasets for analyses using environmental predictors. In some cases, symbols have been shifted slightly to avoid overlap. The inset histograms show the frequency distribution of number of species (upper histogram) and number of sites per metacommunity (lower histogram).
ª 2015 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. (Lancaster and Downes 2013; Heino and Peckarsky 2014) , and the family Chironomidae may alone account for a very large share of species in a dataset (e. g. ,  Raunio et al. 2011). However, given the lack of data for other important taxa, such as beetles (Coleoptera), our results are only applicable to the five taxonomic groups analyzed. Both insect and environmental data were available for 23 basins, while insect data only were available for the remaining eight basins. We initially intended to use datasets consisting of at least 15 stream sites per basin, but included a few basins with a lower number of sites to increase geographic coverage for across-basins beta diversity analyses (see below). The average number of sites per drainage basin was 24.
5 (range: 4 to 213), and most drainage basins included data for at least three insect taxonomic groups (see below) (Fig. 1 ). Each insect dataset was also checked to ensure that surveys were conducted within a single year and that sampling did not span multiple seasons (i. e. , samples within a drainage basin were taken within a short period of time). This was performed to guarantee that we were studying sets of potentially interacting species (see  Leibold et al. 2004 ). In addition, each insect dataset was based on standardized sampling methods (e. g. , kick-netting or Surber samplers), and betweensite differences in species composition were thus comparable within each dataset. We focused on datasets from streams subject to little anthropogenic impact, although in a few basins, some streams were affected by adjacent human land use (e.
g. , some streams drained managed forests). Insect data for each drainage basin were separated by taxonomic group for analyses, yielding a total of 95 datasets, of which 75 included both insect and environmental data. However, in the within-basin analyses described below, we used only 61 datasets that included at least 15 sites. We used this approach because each taxonomic group may show distinct latitudinal patterns in diversity  (Vinson and Hawkins 2003; Pearson and Boyero 2009) , exhibit different dispersal abilities  (Bonada et al. 2012) , or respond differently to environmental factors  (Heino and Mykr€ a 2008). Furthermore, different taxonomic groups may exhibit different patterns of metacommunity organization  (Heino and Mykr€ a 2008; Altermatt et al. 2013). Taxonomic resolution varied among the insect datasets due to differences in taxonomic knowledge among regions; however, all datasets involved identification to at least the level of genus.
Environmental variables The physical and chemical variables that were measured in each drainage basin varied greatly (Appendix S3). We removed some categorical variables from drainage basin datasets where there were fewer sites than explanatory variables. We also pooled fine and coarse estimates of benthic organic matter when both were present in the same dataset. Similarly, for datasets that included proportional coverage of substrate size classes, the finer categories (i. e. , silt, sand, gravel, and pebble) were pooled. We also removed variables that provide similar information (e. g. , we retained only total nitrogen when several forms of nitrogen were reported), as well as variables only measured at a few sites within a drainage basin and some variables related to riparian vegetation (e.
g. , width of the riparian vegetation) that affect stream insects indirectly. After this initial data screening, we finalized environmental datasets for each insect group in each drainage basin. This involved deletion of sites from which a particular insect group was absent and deletion of variables for which observations were only available for a few sites. Finally, we removed entire datasets including <15 sites. The 61 final environmental datasets were derived from 20 basins and included 4-21 variables, and a total of 67 variables were used. Conductivity, total phosphorus, and depth were the most common variables, available for 54, 41, and 40 datasets, respectively (Appendix S3).
Comparative analyses of beta diversity We regressed dissimilarity-based beta diversities obtained for each drainage basin (n = 95) against (i) taxonomic group (as a dummy variable), (ii) latitude (absolute values), (iii) range in altitude, and (iv) spatial extent of a drainage basin. We also included (v) an interaction term between range in altitude and latitude (i. e. , the product of range in altitude and latitude) because the effects of range in altitude may depend on latitude  (Hawkins and Diniz-Filho 2006). In addition to these factors, estimates of beta diversity may be influenced by the properties of the datasets  (Podani and Schmera 2011). Thus, we also included in the regression models the following predictor variables: (vi) number of sites in the dataset, (vii) number of species, (viii) matrix fill (percentage of presences), and (ix) percentage of taxa identified to species level.
Three datasets included many more stream sites than others (Fig. 1 ), so we log-transformed this variable to improve the distribution of residuals. See Figure  2  for the main steps of our statistical approach. We hypothesized that beta diversity should be associated with environmental heterogeneity  (Anderson et al. 2006; Heino et al. 2013 ). However, a variable directly related to environmental heterogeneity could not be Repeated for 61 datasets y 1-95 ~ x 1 + x 2 +. + x 9 Fitting of all combinations of submodels AIC analyses to evaluate importance of variables  , explanatory variables for the across-basins analysis; p, number of taxa in a given biological data matrix; q, q -, and q -, number of environmental variables (E) before, after using VIF and after using forward selection, respectively; r and r -, number of eigenvectors variables (V) before and after using forward selection.
estimated for all datasets because only biological data were available for some of them. Also, the number and type of environmental variables measured in each basin varied. We therefore assessed whether range in altitude could serve as a proxy for environmental variation among stream sites within a drainage basin. We obtained altitudinal range for each dataset from the Shuttle Radar Topography Mission  (Jarvis et al. 2008 ). We then calculated the average coefficient of variation for stream environmental variables within each dataset and correlated it with altitudinal range, revealing a positive correlation (r = 0. 55). We thus opted to use altitudinal range as a surrogate variable for environmental variability or heterogeneity because this metric allowed us to include all 95 datasets in the comparative analyses rather than only the 75 (i.
e. , also including datasets with <15 sites) for which environmental data were available. Altitudinal range is likely to be related to variation in multiple environmental factors affecting the distribution of stream organisms, including temperature, oxygen concentration, and differences in habitat conditions between lowland and mountain streams  (Ward 1998; Jacobsen and Dangles 2012). b 1 b p Bio Geo Env Long lat E 1 E q V 1 V r s n s 1 n s 1 n s 1 n s 1. s n s 1. s n s 1. s n s 1. s n s 1. s n """! s 1 n b 1 b p b 1 b p b 1 b p b 1 b p E 1 E q- V 1 V r- E 1 E q- E 1 E q-- s 1. s n Figure 2. Flow chart Hence, the larger the altitudinal range in a drainage basin, the larger the variation in stream environmental conditions organisms have to cope with. Two metrics of spatial extent were obtained initially.
Explaining variation in the biological datasets within each drainage basin In each drainage basin, spatial variables were generated from the Euclidean distance matrix through Moran eigenvector maps (MEM), formerly called principal coordinates of neighbor matrices  (Borcard and Legendre 2002). The spatial variables (eigenvectors) derived from MEM represent orthogonal patterns of relationships among sampling sites and are used as spatial predictor variables (Fig. 2 ). Eigenvectors with high eigenvalues represent broad-scale patterns, whereas those associated with low eigenvalues represent fine-scale patterns. MEM thus produces multiple spatial variables that are efficient in capturing complex spatial patterns in the response data. As watercourse distances between sites were available in only a few datasets, we had to rely on using overland distances in MEM.
This is likely to be appropriate because all of our taxa have flying adult stages. Although some studies have suggested that watercourse distances are more meaningful for stream organisms than overland distances  (Altermatt et al. 2013) , others have found that these two measures of distance are often strongly correlated, providing virtually the same information about the spatial structuring of a metacommunity  (Thompson and Townsend 2006; Landeiro et al. 2012; Gr€ onroos et al. 2013). We used redundancy analysis (RDA;  Legendre & Legendre, 2012)  to examine the relative contributions of environmental and spatial variables (from MEM) to variation in assemblage composition (using abundance data when available) among sites. Partial RDA (pRDA) was employed to estimate fractions of the total variation of site-by-taxon matrices explained by environmental and spatial variables.