dataesr/scientific-paragraphs-categorization huggingface dataset:
The aforementioned detectors are described below, noting the detector configurations during pp data taking at √ s = 8 TeV in 2012. The EMCal detector  [23]  is a sampling electromagnetic calorimeter. Its active elements, called cells, are composed of 77 alternating layers of lead and plastic scintillator providing a radiation length of 20. 1 X 0. The scintillation light in each layer is collected by wavelength shifting fibers perpendicular to the face of each cell. The fibers are connected to 5 × 5 mm 2 active area Avalanche photo diodes (APDs) to detect the generated scintillation light. Each cell has a size of η × φ = 0. 0143 × 0. 0143 (≈ 6. 0 × 6. 0 cm 2 ), corresponding to approximately twice the Molière radius. Groups of 2×2 cells are combined into modules, which are further combined into arrays of 12×24 modules called supermodules.
Datasets and event selection During the data taking period of pp collisions at √ s = 8 TeV in 2012, the LHC operated at high beam intensities of approximately 2 × 10 14 protons per beam. Collisions at the ALICE interaction point were realized using a so-called "mainsatellite" bunch scheme, which involved proton collisions between the high intensity main bunches and low intensity satellite bunches. The interaction probability per bunchsatellite crossing was about 0. 01, corresponding to an average instantaneous luminosity of about 5 × 10 30 cm -2 s -1. Background events caused by beam-gas interactions or detector noise are rejected in the analysis using the V0A and V0C timing information  [22]. Pileup events, with more than one pp collision per bunch crossing, are rejected based on SPD pileup identification algorithms looking for multiple primary vertices in a single event  [22].
Additionally, the SPD is used to reject background events by comparing the number of SPD clusters to the multiplicity of SPD track candidates found in the respective collision. Only events with a z-vertex position of |z| < 10 cm in the global ALICE coordinate system are accepted for the analysis. Two different types of triggers were used during data taking to select the events to be recorded: the minimum bias (MB) trigger and the calorimeter triggers, which are provided by the EMCal and the PHOS, to enhance statistics at high p T by selectively recording events with high energy deposits in the calorimeters. The MB trigger is a hardware Level-0 (L0) trigger  [29]. It requires at least one hit in each V0A and V0C  [28]. Both calorimeters also provide L0 triggers: EMC-L0  [30]  and PHOS-L0  [31].
0 ± 1. 1, R F PHOS-L0 = (12. 4 ± 1. 5) × 10 3 and R F EMC-L1 = (14. 9 ± 0. 3) × 10 3. The last factor is obtained by multiplying the two given rejection factors of the two EMCal triggers, see Fig. 1 , as the R F for EMC-L1 to MB trigger is of interest. The luminosity determination is based on the cross-section of the MB trigger condition, σ MB AND , measured in a van der Meer (vdM) scan  [34, 35]. The stability of the measured cross section throughout the whole data taking period is assessed by comparing the V0-based luminosity measurement with an independent luminosity signal, issued by the T0 detector. As discussed in Ref. [35] , this comparison results in an overall normalization uncertainty of 2. 6%, which includes contributions from both the vdM-based measurement and its stability over time.
For the PHOS, the energy deposition in each cell is calibrated by adjusting the π 0 peak position in the invariant mass spectra of photon pairs to the true mass of the π 0 meson. The accuracy of this calibration procedure is estimated to be better than 1%. It is evaluated from a comparison of the π 0 peak width in calibrated data and MC simulations by introducing random, normal-distributed decalibration parameters to the MC simulation. Photon identification criteria are applied to the sample of reconstructed clusters in order to primarily select clusters generated by true photon candidates. For the photon reconstruction with PHOS, relatively loose identification cuts are applied because the shower overlap is negligible and the combinatorial background is found to be small in pp collisions.
For the EMCal, all clusters matched with a primary charged track are rejected. This track matching procedure, referred to as general track matching, uses a track p T -dependent matching in η and ϕ, beginning from | η| < 0. 04 and | ϕ| < 0. 09 for very low track momenta of p T < 0. 5 GeV/c and going down to | η| < 0. 01 and | ϕ| < 0. 015 for highest track momenta, using the p T -dependent matching conditions | η| < 0. 01 + ( p T + 4. 07) -2. 5 and | ϕ| < 0. 015 + ( p T + 3. 65) -2. Applying these conditions, a primary track to cluster matching efficiency of more than 95% is obtained over the full p T range, rising above 98% for the analyzed EMCal triggered datasets for p T beyond 123 10 GeV/c. To further enhance the photon purity and to reject neutral hadrons, a cluster shape cut of 0. 1 ≤ σ 2 long ≤ 0.
7 is applied for EMCal clusters, where σ 2 long stands for the smaller eigenvalue of the dispersion matrix of the shower shape ellipse defined by the responding cells and their energy contributions to the cluster  [9, 39]. The lower threshold of σ 2 long is chosen to remove contamination caused by neutrons hitting the APDs of the readout electronics. Photons convert into lepton pairs within the detector material of ALICE with a probability of about 8. 5%. The reconstruction of such photon conversion candidates using PCM may be divided into three major steps: (i) tracking of charged particles and secondary vertex (V 0 ) finding  [37] ; (ii) particle identification and (iii) photon candidate reconstruction and subsequent selection. The V 0 s used in this analysis are obtained during data reconstruction using all available tracking information, recalculating the momenta of the daughter tracks under the assumption that both daughters are created with parallel momentum vectors at the V 0.
Moreover, it includes the trigger efficiency uncertainty for the high energy photon trigger analysis, which is estimated by comparing the trigger turn-on curve from data with MC simulations. "Trigger normalization & pileup" summarizes systematic uncertainties due to the trigger normalization factor and pileup effects. The uncertainty related to the trigger normalization factor is estimated by changing the range of the fit to determine the rejection factor (R F). Furthermore, the out-of-bunch pileup contribution is evaluated by varying the timing cut to accept clusters. For the PCM measurement, the main source of systematic uncertainty is the material budget, for which the same value is used as previously calculated in Ref. [3]. The signal extraction uncertainty is estimated by changing the integration window around the invariant mass peak, the normalization range of the mixed-event background and by using different order polynomials as well as other fit functions to evaluate the remaining background contribution.
The uncertainty is assessed by running the analysis only with/without TRD modules in front of the EMCal, since part of the data taking in 2012 occurred with the EMCal only partially obscured by the TRD. Since TRD and TOF have similar material budgets, the same uncertainty is assigned to the TOF as well, which covered the full polar angle so that a similar assessment as for the TRD is not feasible. Both uncertainties are quadratically combined to arrive at the given uncertainties which are listed in the tables. The signal extraction uncertainty contains the systematic uncertainties obtained from variations of the background normalization region, the choice of the background fit function and integration intervals, analog to the PCM method, as well as from variations of the minimum opening angle cut on the meson level.
Acknowledgements We thank  Werner Vogelsang  and  Marco Strat-del Personal Academico (DGAPA) ,  Mexico ;  Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO) ,  Netherlands ;  The Research Council of Norway, Norway; Commission on Science and Technology for Sustainable Development in the South (COMSATS), Pakistan ;  Pontificia Universidad Católica del Perú, Peru; Ministry of Science and Higher Education and National Science Centre, Poland ;  Korea Institute of Science and Technology Information and National Research Foundation of Korea (NRF) ,  Republic of Korea; Ministry of Education and Scientific Research, Institute of Atomic Physics and Romanian National Agency for Science, Technology and Innovation ,  Romania ;  Joint Institute for Nuclear Research (JINR) ,  Ministry of Education and Science of the Russian Federation and National Research Centre Kurchatov Institute, Russia; Ministry of Education, Science, Research and Sport of the Slovak Republic , Slovakia;  National Research Foundation of South Africa ,  South Africa ;  Centro de Aplicaciones Tecnológicas y Desarrollo Nuclear (CEADEN) ,  Cubaenergía ,  Cuba, Ministerio de Ciencia e Innovacion and Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT), Spain; Swedish Research Council (VR)  and  Knut & Alice Wallenberg Foundation (KAW), Sweden ;  European Organization for Nuclear Research, Switzerland ;  National Science and Technology Development Agency (NSDTA) ,  Suranaree University of Technology (SUT)  and  Office of the Higher Education Commission  under  NRU  project of Thailand, Thailand;  Turkish Atomic Energy Agency (TAEK) ,  Turkey ;  National Academy of Sciences of Ukraine, Ukraine ;  Science and Technology Facilities Council (STFC) ,  United Kingdom ;  National Science Foundation  of the  United States of America (NSF)  and  United States Department of Energy, Office of Nuclear Physics (DOE NP) ,  United States of America.
Since the presentation of the Radiation Model, much work has been done to compare its findings with those obtained from Gravitational Models. These comparisons always aim at measuring the accuracy with which the models reproduce the mobility described by origin-destination matrices. This has been done at different spatial scales using different datasets, and several versions of the models have been proposed to adjust to various spatial systems. However the models, to our knowledge, have never been compared with respect to policy testing scenarios. For this reason, here we use the models to analyze the impact of the introduction of a new transportation network, a Bus Rapid Transport system, in the city of Teresina in Brazil. We do this by measuring the estimated variation in the trip distribution, and formulate an accessibility to employment indicator for the different zones of the city.
Introduction Assessing the impact of new infrastructural projects is a challenging and demanding task that requires knowledge or estimates of the mobility of the individuals living in the city. Many models have been developed to this effect  [1, 2, 3, 4] , focusing on different scales of the urban system, according to the quality of the data available. Traditionally, these models allocate trips from one geographical zone to another, according to estimates of where people live and work. Infrastructure projects are then assessed following changes in accessibility which are computed from the model's predictions of the people living and working in these zones. Among the models that have been proposed over the decades, the gravitational model  [4]  has been one of the most widely adopted in various contexts (for example,  [5, 6, 7, 8, 9, 10] ).
These studies show how in general, the accuracy in reproducing the observed flows of the extended and the normalised version are often comparable to those obtained from the doubly-constrained gravitational model. The origin-destination matrices used to compare the models in previous works, are extracted both by conventional datasets, as for example mobility surveys and census data, or unconventional datasets such as mobile phone or geo-located social media data. Very often though the matrices are outdated, incomplete or obtained by biased samples of the population  [19, ?]. We therefore believe that the models need to be compared in a more practical manner where their results can be read and interpreted more easily. For this reason, here we want to take a different approach and exploit the two different models to quantify and predict the impact of the introduction of a new Bus Rapid Transit (BRT) system in the city of Teresina in Brazil.
Results In the first part of this section, we will produce the trip duration distribution with the different models and calibrate it to the trip data (details of which with respect to the origin-destination matrix data are to be found in the appendix). As said, the reproduction of the observed origin destination matrices is usually how the goodness of fit of such a model is tested. From this analysis, it will be clear that the normalized radiation model is not appropriate in fitting mobility problems at this scale (as it has not been developed with intra city trips in mind), and will therefore, we will not consider this model further. In the second part, we will compare in great detail the results obtained when calculating the impact on the accessibility of the new BRT bus network. We will do this only using the extended version of the radiation and the gravitational models.
Trip Duration Distributions To calibrate the parameters of the models we have used we used the maximum likelihood method. As one can see in the appendix the origin-destination survey counts only 138 households, and therefore does not contain information on trips from every zone to every other. For each origin zone, we only have information on trips to a limited number of destinations. This implies that we cannot calibrate the models using the common part of commuters based on the Sorensen's index  [37]  as done in previous comparisons [?,  16, 17, 18]. We therefore extract the trip duration distribution of the bus trips contained in the dataset [30] , p obs (t) and for each value of the parameter which we generically indicate as p, we measure the distribution of the predicted duration of the trips p mod (t, p).
Data The main data source is the STRANS/PMT (Teresina Transport Authority) database created through an Origin-Destination survey conducted in 2007 including the cities of Teresina and Timon  [30]. The database includes household conditions, personal socioeconomic information and travel diaries per person on the day before the survey was taken. The main dataset contains trip data (walking times, waiting times, travel times, mode, origin and destination zones and activities, and trip costs) combined with disaggregated demographic and socioeconomic information about the traveler (education level, income, gender, employment, age) and traveler's household data (traffic zone, comfort and deprivation variables -number of families, bedrooms, bathrooms, sewage system, access to water, energy consumption).
The survey consists of 64 traffic zones in Teresina and Timon that coincide with the cities' districts which provide the opportunity to gather sociodemographic data from the national census. In total, the dataset contains 5,177 journeys distributed across 138 households. The dataset also contains information about the number of employers, students and total population in each traffic zone. Geo-referenced data is also available for bus routes and stops for all routes in Teresina and Timon and the General Transit Feed Specification (GTFS) for the public transportation in the city. Data about Timon's buses routes were taken from Moovit App. The Bus Journeys Dataset was also taken from STRANS/PMT and contains 45,090 bus trips for a twenty-four hour interval for each bus line in 2006. The database describes single bus passengers journey through the variables: Bus Route, Bus ID, Direction, Time at Origin, Origin Bus Stop and Destination bus stop.
The results were stored in the Origin Destination cost matrix C old. On the other hand the travel times after the BRT introduction had to be predicted. To estimate new routes, we have built a model using ArcGIS Spatial Analyst Extension for the existing bus network with the current local bus GTFS (General Transit Feed Specification, given by the Transport Authority of Teresina), and calibrated with real travel time data and the predicted travel time between pairs of zones was calculated. Further developments for the BRT systems were introduced in the network (BRT GTFS), and the predicted travel time between pairs of zones was calculated. In this case the results were stored in the origin destination cost matrix C brt ij. Data Availability: All the data used in this work can be found in this public repository https://github.
com/ducciopiovani/D on-Teresina-, where we have stored the cost matrices and the origin and destination matrix in 3 separate files. Competing Interests:We declare we have no competing interests. Authors' Contributions: All authors conceived and designed the study, analyzed and interpreted the results and drafted the manuscript. D. P. carried out the analysis, written all the codes both for the simulations and the data analysis, and coordinated the efforts. G. U. gathered and manipulated the data, built the cost matrices and wrote the first draft. Funding: All authors acknowledge the funding from the Engineering and Physical Sciences Research Council, grant number: EP/M023583/1.
BAC contigs from this STP were oriented and merged into 10 mosaic super-scaffolds based on synteny conservation with sorghum. This STP consisted of 382 Mb of high-quality sequence in 3965 contigs, with an average size of 96 kb, and including only 0. 02% uncalled bases (N). A total of 25,316 protein-coding gene models were predicted, which represented 13% of the STP sequence (Table  1 , Supplementary Table  2 ). A sorghum orthologous gene could be found for 20,809 (82%) of these sugarcane-predicted genes among the 27,532 sorghum protein-coding gene models predicted on the sorghum genome. Many of the remaining 4507 gene models were short monoexonic predictions with no hits in the Uniprot/Trembl sequence database (http://www. uniprot. org) and no Interpro domains and likely corresponded to overpredictions.
Whole BAC sequence contigs were aligned to the sorghum sequence using the BLAST algorithm with the best hits being retained to produce Fig. 4. To construct STP, contigs (larger than or equal to 20 kb) from the 4535 sequenced BAC clones were aligned with the sorghum-annotated nucleotide gene set (version 3. 1) using BLAT (https://github. com/dib-lab/ged-docs/wiki/ BLAT, parameters -noHead -extendThroughN -q=rna -t=dna). Sorghum gene alignments were used to inform the process of reducing the initial MTP in an STP representing a single copy of sugarcane gene space. BAC segments sharing at least two sorghum genes were considered as redundant overlapping regions. These BAC clones were aligned with one another using Gepard (http://cube. univie. ac. at/ gepard). Overlapping BAC regions sharing identical sorghum genes were then collapsed into a single path, thus maximizing the number of retained genes and minimizing sequence loss.
Three cDNA libraries (one per tissue) were built using the TruSeqStranded mRNA sample preparation kit (Illumina) and subsequently paired-end sequenced (2 × 125 bp) on an Illumina HiSeq 2500 system. A total of 354 million reads were produced and 63, 54, and 57 million paired-read clusters were built for leaves, roots, and stems, respectively, and assembled using Trinity in 340,409 transcripts larger than 300 bp (https://github. com/trinityrnaseq/trinityrnaseq/wiki). Sugarcane genes annotation. Structural annotation was performed using EuGene 4. 2a (http://eugene. toulouse. inra. fr), which combines evidence from alternative splice site detection, sequence model training, genome masking, transcript mapping, and protein homology to define confident gene models. The R570 cultivar RNA transcripts, together with transcripts from six sugarcane genotypes produced by Cardoso-Silva et al.
65  , were aligned on the sugarcane STP using GMAP (https:// github. com/juliangehring/GMAP-GSNAP/) and best-scoring hits were kept. The resulting alignments were integrated as sugarcane transcript evidence in the prediction process. Protein sequences from the reference sorghum proteome (Phytozome, release 3. 1. 1) were aligned with the sugarcane STP using BLASTX to obtain protein homology evidence. Gene function annotation was assigned based on sequence and domain conservation. Protein sequences were aligned with SwissProt, TrEMBL, and sorghum datasets by BLASTP using an E-value threshold of 1e-10. Best-hit BLAST results were then used to define the gene functions. Moreover, InterProScan = (https://www. ebi. ac. uk/interpro/search/sequence-search) was performed to annotate protein domains, extending the annotation to Gene Ontology terms associated with these protein domains.
The predicted gene models were finally screened to remove models that could correspond to TEs using CENSOR (https:// www. girinst. org/censor/)  66 resulting in a total of 25,316 sugarcane genes annotated on the STP. TEs annotation. Sets of 2 million Illumina reads (150 bp) were retrieved from WGS data for the R570 cultivar, one S. officinarum accession (LA Purple) and one S. spontaneum accession (SES 234). A similar dataset was created from the sugarcane STP through generation of in silico Illumina reads with WGSIM (https:// github. com/lh3/wgsim). These four datasets were used to perform a graph-based clustering analysis of repetitive sequences using the RepeatExplorer pipeline  67. A total of 179 clusters were obtained, manually inspected using the. htlm summary output file generated by the pipeline, and clusters corresponding to chloroplast or containing less than 3000 Illumina reads were not considered for further analyses.
The 135 remaining clusters were manually annotated using a library of 58 previously annotated sugarcane elements  52  and using similarity searches against Repbase (https://www. girinst. org/repbase/), a protein domain database of plant mobile elements (included in RepeatExplorer). For each annotated cluster, the WGS read numbers and percentage were calculated in datasets corresponding to STP, R570, S. officinarum, and S. spontaneum (Table  2 ). TE annotation of the sugarcane STP was performed with the TEannot pipeline from the REPET package v2. 5 (https://urgi. versailles. inra. fr/Tools/REPET), which requires a reference library of TEs. This latter reference library was built through clustering of the following TE sequences: -The 58 previously annotated sugarcane TEs. -1883 full-length LTR retrotransposons detected in the sugarcane STP using LTR_FINDER v1.
05 (https://github. com/xzhub/LTR_Finder). -The 135 RepeatExplorer clusters described above. A total of 446 clusters were obtained using UCLUST v1. 2. 22q with the following parameters: --id 0. 80 --maxlen 25000 --minlen 100 -nucleo (https://drive5. com/ usearch/manual/uclust_algo. html) and used for TE annotation with the REPET package. The percentage of TE in the STP was calculated based on the sequence proportion covered by each TE type (Table  2 ). The timing of insertion of full-length LTR retrotransposons was determined by estimating the amount of divergence between LTR sequences. The two LTRs of each retrotransposon were aligned using MAFFT (https://mafft. cbrc. jp/alignment/ software/), and the genetic divergence was estimated using the Kimura 2 parameter method. The insertion dates were estimated using a substitution rate of 1.