dataesr/scientific-paragraphs-categorization huggingface dataset:
The p miss T variable is recalculated ignoring the photon, to mimic the boson recoil. In addition to the desired γ þ jets events with a genuine well-identified and isolated ("prompt") photon, small contributions from QCD multijet events with hadronic jets misidentified as photons are present in this region ("nonprompt"). To estimate this background contribution, a purity measurement is performed. The measurement is based on the distribution of the lateral width, σ iηiη  [53] , of the ECAL supercluster associated with the photon. For prompt photons, the distribution of σ iηiη peaks sharply around values of 0. 01 and below, while nonprompt photons show a much smaller peak and a shoulder toward values larger than 0. 01. To extract the contamination, a template fit to the σ iηiη distribution is performed in data collected with a looser version of the CR selection.
The final QCD multijet contribution is then determined by weighting the events observed in the data by the nonprompt fraction. A 25% uncertainty is assigned to the normalization of the QCD multijet background to account for any mismodeling of σ iηiη in the simulation. The uncertainty is estimated by repeating the measurement while varying the binning of the σ iηiη distribution used for fitting, capturing the effect of the mismodeling. The statistical uncertainty in the determination of the differential m jj shape is negligible.
D. Multijet background The QCD multijet background is estimated using events in data in which the p miss T arises from mismeasured jets. Depending on the source of the mismeasurement, the jet that was mismeasured is either balanced in the case of additional HF noise or aligned with the p miss T in ϕ. For the multijet background stemming from HF noise, an m jj template is extracted by inverting the requirements on the HF jet shape variables, hence requiring at least one jet in the event to fail the selection criteria given in Table  I. The probability for an HF noise jet candidate to pass or fail the criteria is parametrized as a function of the jet p T and η, using events selected to have large p miss T and to contain only one HF jet balanced in ϕ with the ⃗p miss T. An event-by-event weight is applied to estimate the contribution in the SR from the "failing" events.
For the second category of multijet events, the requirement on minðΔϕð ⃗p miss T ; ⃗p jet T ÞÞ is inverted to define a control region enriched in QCD multijet events (QCD CR). The m jj shape for the contribution of this background in the SR is derived from the yields in data in each m jj bin in the QCD CR, after subtracting the contributions from V þ jets, diboson, and top quark processes estimated from simulation, as well as HF noise contributions estimated in the data. The template is normalized as follows. The distribution of minðΔϕð ⃗p miss T ; ⃗p jet T ÞÞ in data is fit with the sum of templates derived from the simulated V þ jets, diboson, and top quark events; a HF noise template derived in data; and a functional form f QCD representing the QCD multijet contribution. The functional form is f QCD ðxÞ ¼ p 0 e -p 1 x ð1Þ for the MTR region and f QCD ðxÞ ¼ p 0 e - ðx-p 1 Þ 2 2p 2 2 ð2Þ for the VTR region.
The parameters p i are allowed to float, and x ¼ minðΔϕð ⃗p miss T ; ⃗p jet T ÞÞ. The choices of these functions are validated by fitting this model to simulated QCD multijet events, and they are found to describe the distributions in the MTR and VTR categories well. The normalizations of the WðlνÞ þ jets, ZðννÞ þ jets, and HF noise contributions are allowed to vary independently in the fit. They are constrained within 20% of the prediction from simulation to account for systematic uncertainties related to jet energy calibrations, missing higher orders in the V þ jets cross section calculations, and the closure of the HF noise contribution between data and simulation. The fitted values of the normalizations are used when subtracting the WðlνÞ þ jets, ZðννÞ þ jets, and HF noise contributions from the data to obtain the m jj template.
Their fitted uncertainties are included in the final systematic uncertainty in the QCD multijet estimate. In both the MTR and VTR categories, the fit range is 0 < minðΔϕð ⃗p miss T ; ⃗p jet T ÞÞ < 1. 8, chosen to minimize the overlap of events in data with the SR. The fits are performed separately for the 2017 and 2018 datasets. Figure  4  shows the minðΔϕð ⃗p miss T ; ⃗p jet T ÞÞ distribution in data used in the fit and the contributions from V þ jets, diboson plus top quark processes, HF noise, and QCD multijet events resulting from the fit. The sums of the 2017 and 2018 datasets are shown for the MTR and VTR categories. The function f QCD ðxÞ provides an estimate of the QCD multijet template normalization, N, in the SR via N ¼ Z π X f QCD ðxÞdx; ð3Þ where X ¼ 0. 5 for the MTR categories and X ¼ 1.
A. Experimental uncertainties The reconstruction and identification efficiencies of electrons, photons, muons, τ h candidates, and b-tagged jets have been measured in both data and simulation  [53, 54, 63, 67]. The simulated events are corrected by scale factors, which are usually dependent on the p T and η of the object, and have associated systematic uncertainties. The scale factors, and their uncertainties, are also propagated when vetoing events with identified objects. For the electrons and muons, when different working points are chosen between leptons selected in the CRs and vetoed in the SR, the corresponding uncertainties are kept uncorrelated. All these uncertainties are correlated between the MTR and VTR categories. Except for the muons, where the dominant source of systematic uncertainty comes from the experimental method employed, these uncertainties are considered uncorrelated between the 2017 and 2018 datasets.
The effects of the jet energy scale (JES) and jet energy resolution (JER) uncertainties are studied explicitly by varying them by one standard deviation  [58] , propagating the changes to the p miss T accordingly, and checking the impact on the transfer factors for the V þ jets processes as functions of m jj. For the signal and the minor backgrounds, the impact is studied on the simulated yields in the SR as a function of m jj , with a fit procedure to remove the statistical contribution in the less-populated high-m jj bins. The JER uncertainties impact the signal yields by 4%-9% (2%-15%), increasing with m jj , for VBF (ggH) production. The impacts of the JES uncertainties are 5%-25% (7%-35%), increasing with m jj , for the VBF (ggH) process. Eleven independent JES sources are considered, with partial correlations between the 2017 and 2018 datasets.
The dominant source is the η dependence of the corrections. The corrections become particularly large for forward jets, which explains the large increase at high m jj. The JER uncertainties are uncorrelated between the two data-taking years. Both the JER and JES uncertainties are correlated between the MTR and VTR categories. Simulated events are weighted to match the distribution of reconstructed vertices to the distribution observed in the data. An uncertainty associated with this procedure is obtained by varying the total inelastic pp cross section by AE5%  [68]  and repeating the background estimation procedure. This uncertainty is correlated across categories and datasets. The trigger efficiencies are measured in both data and simulation, and a scale factor is extracted. For the signal triggers, the scale factor is parametrized as a function of the p miss T.
The associated systematic uncertainty partially cancels in the transfer factors between the muon CRs and the SR. The associated uncertainty is uncorrelated across categories as different triggers are used but partially correlated between years. For the electron CRs, the scale factor is parametrized as a function of the lepton p T and η, and the impact of the uncertainties is propagated to the corresponding expectations in the SR. The uncertainty in the electron trigger scale factor is uncorrelated between years but correlated across categories. The uncertainty in the integrated luminosity of the 2017 (2018) dataset is 2. 3%  [69]  (2. 5%  [70] ). When combined together with the 2016 dataset  [71] , the uncertainty is reduced to 1. 6%. The improvement in the precision reflects the (uncorrelated) time evolution of some systematic effects.
VII. RESULTS A binned maximum likelihood fit is performed simultaneously across the SR and all CRs in both categories and for both datasets. In the fit, one parameter per bin i of the m jj distribution, for each category and year, is left freely floating. This parameter represents the expected rate of the background from the strong production of ZðννÞ þ jets events, and it is labeled κ i νν. The m jj distribution is binned in the same way as in Ref. [22]. This binning has been found to be optimal for the 2017 and 2018 datasets as well. Similar to the method described in Ref. [22] , the likelihood function is defined as Lðμ; κ νν ; θÞ ¼ Y i Pðd i jB i ðθÞ þ Z i ðκ i νν Þ þ W i ðκ i νν ; θÞ þ μS i ðθÞÞ × Y CR Y i Pðd CR i jB CR i ðθÞ þ V CR;strong i ðκ i νν ; θÞ þ V CR;VBF i ðκ i νν ; θÞÞ Y j Pðθ j Þ; Z i ðκ i νν Þ ¼ ð1 þ Z VBF strong i Þκ i νν ; W i ðκ i νν ; θÞ ¼ ðf W=Z;strong i ðθÞ þ Z VBF strong i f W=Z;VBF i ðθÞÞκ i νν ; V CR;strong i ðκ i νν ; θÞ ¼ C CR;strong i ðθÞR CR;strong i ðθÞκ i νν ; V CR;VBF i ðκ i νν ; θÞ ¼ C CR;VBF i ðθÞZ VBF strong i R CR;VBF i ðθÞκ i νν ; ð4Þ where PðxjyÞ ¼ y x e -y =x! and d CR i and d i are the observed number of events in each bin i of the m jj distribution in each of the CRs and in the SR, respectively.
The results are presented for the MTR and VTR categories separately. They are shown separately for the 2017 and 2018 datasets when presenting the transfer factors, and for the datasets added together otherwise. The corresponding figures and tables split into individual control regions and data-taking periods are available in the Supplemental Material  [66]. All results are obtained from a combined fit across all categories and datasets. For the maximum likelihood fit, the different datasets and categories are treated separately. The final results are obtained from a fit using the combined likelihood function including all categories and datasets, which takes into account nuisance correlations.
A. Control regions for the MTR category The ratio of dilepton to single-lepton events is studied to validate the predictions from simulation and uncertainties used to model the ratio of Z þ jets to W þ jets events in the SR [parameters f W=Z;proc i ðθÞ in Eq. (  4 )]. The prefit ratio between the number of Z þ jets and W þ jets events in the CRs in bins of m jj is shown in Fig. 5  (upper row) for the 2017 (left) and 2018 (right) datasets. The prediction is compared to the ratio of observed data yields, from which the estimates of minor background processes have been subtracted. The CR-postfit results are shown together with the prefit ratio. A reasonable agreement is observed between data and simulation, with differences in most bins covered by the systematic uncertainties listed in Table  III   In 2017, the simulation predicts a lower ratio than observed in data.
The compatibility of the data with the prefit prediction is measured, in this particular category and year only, using a χ 2 test accounting for correlations between the 2017 MTR CRs and each m jj bin. This test indicates that there is a local discrepancy of approximately two standard deviations. The disagreement is attributed to the Z þ jets regions with low event yields and is partially compensated in the fit through the movement of nuisance parameters representing uncertainties such as the renormalization and factorization scales. The significance of the discrepancy is low, and none of the nuisance parameters moves by more than one standard deviation from their prefit value in the combined fit across all categories and years. The p-value  [72]  for the 2017 dataset in the MTR category after the combined fit is 38.
4%, and it is 37. 0% for all categories when combining the 2017 and 2018 datasets. Closure tests have also been performed comparing the decays to electrons or muons separately for the W and Z CRs, again showing reasonable agreement between data and simulation. The ratio of photon to dilepton events is also studied to validate the predictions from simulation and uncertainties used to model the ratio of γ þ jets to Z þ jets events in the SR [parameters f γ=Z;proc i ðθÞ in Eq. (  4 )]. The prefit and CR-postfit ratios between the number of events in the γ þ jets over Z þ jets CRs in bins of m jj are shown in Fig. 5  (lower row) for the 2017 (left) and 2018 (right) datasets. The prediction is compared to the ratio of observed data yields, from which the estimates of minor background processes have been subtracted.
A similar observation can be made for the 2017 MTR category as for the Z þ jets over CMS   III , as well as the statistical uncertainty in the simulation. W þ jets ratio. This effect is again attributed to the Z þ jets CRs with low event yields. VTR ( 13 TeV) The m jj distributions in data in the dilepton and single-lepton CRs, along with the postfit estimates of the background contributions, are shown in Fig. 6. Similar distributions in the photon CR are shown in Fig. 7. The total background estimated from a fit assuming BðH → invÞ ¼ 0 is also shown. The distributions show the sum of the 2017 and 2018 datasets in each region. The postfit predictions are in good agreement with the data within one standard deviation for most of the bins, with discrepancies of just over one standard deviation in the dielectron MTR CR for two bins at m jj ≈ 2 TeV.
The yields from the 2017 and 2018 samples are summed, and the correlations between their uncertainties are neglected. with a profile likelihood ratio test statistic  [75]  in which systematic uncertainties are modeled as nuisance parameters following a frequentist approach  [76]. m Both VBF and non-VBF signal production modes are included, with their relative contributions fixed to the SM prediction within their uncertainties. Between the 2017 and 2018 datasets, and the two analysis categories (MTR and VTR), the uncertainties are correlated according to the description given in Sec. VI. To combine with the data taken in 2016, the same correlation scheme as between 2017 and 2018 is used, except for the jet energy calibration uncertainties (JES and JER), which are kept fully uncorrelated. The integrated luminosity of the 2016 dataset was updated to 36.
3 fb -1 to reflect the latest improvements in the luminosity measurement  [71]. To be consistent with the treatment of the VBF signal in the 2017 and 2018 analyses, the Higgs boson p Tdependent EW NLO corrections are also applied to the 2016 signal shape. The VBF results obtained with the earlier datasets, namely the dataset from 2012 (2015), at ffiffi ffi s p ¼ 8ð13Þ TeV, with 19. 7 (2. 3)fb -1 , from Ref. [27] , are combined, taking into account uncertainty correlations where appropriate. Theoretical uncertainties related to signal modeling are correlated for data-taking periods with the same center-of-mass energy. Partial correlations between datasets exist for the uncertainty in the luminosity measurements. All other experimental uncertainties are decorrelated between the run periods before and after 2015.
Constraints on an SM-like Higgs boson Observed and expected upper limits on ðσ H =σ SM H Þ× BðH → invÞ at 95% C. L. are presented in Fig. 11  and Table VI. The limits are computed for the combination of all datasets, as well as for individual categories and datataking periods. By itself, the addition of the γ þ jets CR (the addition of the VTR category) improves the expected limits by about 11% (8%) compared with the 2016-like analysis selections. Considered together, the improvements reach about 17% in both years. The upper limits for the individual categories entering the combination are available in Supplemental Material  [66]. The combination of the 2017 and 2018 results yields an observed (expected) upper limit of BðH → invÞ < 0. 18ð0. 12Þ at the 95% C. L. , assuming an SM Higgs boson with a mass of 125.
18 (0. 12) at the 95% C. L. , assuming the standard model production cross section. The results are combined with previous measurements in the vector boson fusion topology, for total integrated luminosities of 19. 7 fb -1 at ffiffi ffi s p ¼ 8 TeV and 140 fb -1 at ffiffi ffi s p ¼ 13 TeV, yielding an observed (expected) upper limit of 0. 18 (0. 10) at the 95% C. L. This is currently the most stringent limit on BðH → invÞ. Finally, the results are interpreted in the context of Higgs-portal models. The 90% C. L. upper limits FIG. 12. Profile likelihood ratios, as functions of BðH → invÞ. The observed likelihood scans are reported for the full combination of 2012-2018 data, as well as for the individual years. The expected results for the combination are obtained using an Asimov dataset  [75]  with BðH → invÞ ¼ 0.
ACKNOWLEDGMENTS We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at  CERN  and at other  CMS institutes  for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centers and personnel of the Worldwide LHC Computing Grid and other centers for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC, the  CMS detector , and the supporting computing infrastructure provided by the following funding agencies:  BMBWF  and  FWF (Austria) ;  FNRS  and  FWO (Belgium) ;  CNPq ,  CAPES ,  FAPERJ ,  FAPERGS , and  FAPESP (Brazil) ;  MES  and  BNSF (Bulgaria) ;  CERN ;  CAS, MoST , and  NSFC (China) ;  MINCIENCIAS (Colombia) ;  MSES  and  CSF (Croatia) ;  RIF (Cyprus) ;  SENESCYT (Ecuador) ;  MoER ,  ERC PUT , and  ERDF (Estonia) ;  Academy of Finland ,  MEC , and  HIP (Finland) ;  CEA  and  CNRS/IN2P3  (France);  BMBF ,  DFG , and  HGF (Germany) ;  GSRI (Greece) ;  NKFIA (Hungary) ;  DAE  and  DST (India) ;  IPM (Iran) ;  SFI (Ireland) ;  INFN (Italy) ;  MSIP  and  NRF (Republic of Korea) ;  MES (Latvia) ;  LAS (Lithuania) ;  MOE  and  UM (Malaysia) ;  BUAP ,  CINVESTAV ,  CONACYT ,  LNS ,  SEP , and  UASLP-FAI (Mexico) ;  MOS (Montenegro) ;  MBIE (New Zealand );  PAEC (Pakistan) ;  MSHE  and  NSC (Poland) ;  FCT (Portugal) ;  JINR (Dubna) ;  MON, RosAtom ,  RAS ,  RFBR , and  NRC KI (Russia) ;  MESTD (Serbia) ;  MCIN/AEI  and  PCTI (Spain) ;  MOSTR (Sri Lanka) ;  Swiss Funding Agencies (Switzerland) ;  MST (Taipei) ; ThEPCenter, IPST, STAR, and  NSTDA (Thailand) ;  TUBITAK  and  TAEK (Turkey) ;  NASU (Ukraine) ;  STFC  ( United Kingdom );  DOE  and  NSF (USA).
B. cinerea infections induce premature expression of ripening-related genes in unripe fruit We performed an RNAseq analysis to identify genes or pathways that could explain the accelerated ripening observed in the B. cinerea-inoculated MG fruit. We hypothesized that most physiological changes observed from 4 dpi onwards were preceded by a large transcriptional reprogramming in the fruit. Therefore, we proceeded to sequence mRNA from mock-inoculated, B. cinerea-inoculated, and healthy MG fruit (i. e. , not wounded) after 3 dpi or 3 days post-harvest (dph). Compared to healthy fruit, we expected to see induction of ripening-related transcriptional activity by B. cinerea inoculation, but not mock inoculation. We also incorporated two other existing transcriptomic datasets: (i) mock-inoculated, B.
cinerea-inoculated, and healthy samples created identically to the 3 dpi samples but sequenced at 1 dpi  (Silva et al. , 2021)  to account for the possibility that genes were triggered earlier during the inoculation, and (ii) publicly available samples of healthy fruit at five developmental stages from MG to red ripe (RR) from the fruitENCODE database  (Lü et al. , 2018)  to capture ripening-related genes. When compared to healthy MG fruit, 5,512 genes were found to be differentially expressed (P adj < 0. 05) as a result of B. cinerea inoculation (MG I / MG H) at either 1 or 3 dpi. In contrast, a much smaller number of genes (582) were differentially expressed due to mock inoculation (MG M / MG H), and most of these (482 or 82. 8%) had the same expression pattern as they had in B. cinerea-inoculated fruit (Table  1 ).
Accession Numbers The novel datasets for this study have been deposited in the Gene Expression Omnibus (GEO) database under the accession GSE183836. treatments; AHRD = Automated Assignment of Human Readable Descriptions; KEGG = Kyoto Encyclopedia of Genes and Genomes. Table 1:        1 and 3 dpi, as well as healthy ripening obtained from the fruitENCODE  (Lü et al. , 2018).
This work was supported by start-up funds from the  College of Agricultural and Environmental Sciences  and the  Department of Plant Sciences (UC Davis ) to BB-U. Funding to CJS was partially provided by the  Plant Sciences Graduate Student Researcher Award  ( UC Davis ). This research was also supported by the  National Science Foundation  under grants  IOS 0544504  and  IOS   0957264  awarded to ALTP. was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
Historical climate forcing We chose the CRUJRA reanalysis product  (Harris, 2019)  as the reference dataset to compare with the unconstrained CMIP6 results, as well as to derive bias corrections and ensemble weights. CRUJRA is derived from the Climatic Research Unit gridded Time Series (CRU TS) v4. 03 monthly data  (Harris et al. , 2014)  and from the Japanese 55-year Reanalysis data (JRA-55)  (Kobayashi et al. , 2015). Temperature, downward solar radiation flux, specific humidity and precipitation in JRA-55 are.
120 ERA5 is the fifth generation reanalysis from the European Centre for Medium-Range Weather Forecasts (ECMWF;  Hersbach et al. , 2020). It uses a linearized quadratic 4D-var assimilation scheme that takes the timing of the observations and model evolution within the assimilation window into account. Compared to the predecessor ERA-Interim reanalysis, it has a higher spatiotemporal resolution and assimilates more observations. The reanalysis is produced at an hourly time step and covers the time period 1979-2020. Its horizontal resolution is 0. 05 •. As for the CRUJRA reanalysis, we aggregated the data to a daily 125 timestep and regridded the dataset to a 0. 5 • spatial resolution using first-order conservative regridding.
Skill An intuitive way to select CMIP GCMs is to define a set of performance metrics and select those GCMs with a pre-defined level of skill (e. g. Rowell et al. , 2016; Gershunov et al. , 2019). We calculated the metrics suggested by  Haughton et al. (2018)  (see tab. 2) using the CRUJRA reanalysis as the reference dataset for daily, monthly and annual precipitation, then ranked all GCMs for each metric and finally chose the GCMs with the highest average rank for monthly and annual timescales. For the last method (overlap of histogram), we estimated the intervals ('bin size') using the Freedman Diaconis Estimator for the reference dataset (CRUJRA) and then used the same bin size for the simulated variable (i. e. CMIP forcing). Table  2. Metrics used to evaluate GCM performance (compare  Haughton et al.
Step 2: Bias correction methods Once a selection of GCMs is made, the biases of a given GCM can be corrected (see fig. 1  step 2). We explored six approaches using CRUJRA as our reference dataset. We corrected the three climate forcing variables, i. e. temperature, precipitation and incoming shortwave radiation, and derived the correction based on the calibration time period 1989-2010 given this is common to both reanalysis products used here. We applied each method per pixel so that the different grid points were corrected independently of each other and tested the correction on both daily and monthly timescales. We show the corrections based on daily timescales in the main figures, and use the corrections based on monthly timescales to assess the sensitivity to the correction timescale in the supplement.