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Record W4322491982 · doi:10.1103/physrevc.107.025502

Single-pion production in electron-proton interactions

2023· article· en· W4322491982 on OpenAlexafffund
M. Kabirnezhad

Bibliographic record

VenuePhysical review. C · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicParticle physics theoretical and experimental studies
Canadian institutionsYork University
FundersFermilabNatural Sciences and Engineering Research Council of CanadaRoyal Commission for the Exhibition of 1851
KeywordsPhysicsParticle physicsPerturbative QCDPionNucleonHadronNuclear physicsProtonMomentum transferNeutrinoElectronInvariant massQuarkQuantum chromodynamicsBaryonScatteringQuantum mechanics

Abstract

fetched live from OpenAlex

This paper presents an extension of the MK single-pion production model [Kabirnezhad, Phys. Rev. D 97, 013002 (2018); Phys. Rev. D 102, 053009 (2020)] to high hadron invariant mass ($W$) and high momentum transfer (${Q}^{2}$) to conform to the predictions of perturbative QCD due to the quark-hadron duality evidence. New form factors for several resonances and nonresonant background in the electron-nucleon cross sections are determined taking into account the experimental data and improved evaluation techniques. Fits to electron-proton scattering data are used to constrain free parameters and to assign the related uncertainties of the model. The results from this work can be used to determine the vector current in the corresponding neutrino-nucleon cross sections, which is an important input for event generators in long-baseline neutrino-oscillation measurements.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.027
GPT teacher head0.366
Teacher spread0.339 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2023
Admission routes2
Has abstractyes

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