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Record W2728419972 · doi:10.1103/physics.10.72

Scattering Experiments Tease Out the Strong Force

2017· article· en· W2728419972 on OpenAlexfundno aff
M. Hjorth‐Jensen

Bibliographic record

VenuePhysics · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNuclear physics research studies
Canadian institutionsnot available
FundersOak Ridge National LaboratoryLawrence Livermore National LaboratoryNuclear PhysicsNatural Sciences and Engineering Research Council of CanadaOffice of ScienceNational Research Council CanadaCompute CanadaDeutsche ForschungsgemeinschaftTRIUMFU.S. Department of Energy
KeywordsScatteringPhysicsCarbon fibersNuclear physicsMaterials scienceOptics

Abstract

fetched live from OpenAlex

How does nature hold together protons and neutrons to form the wide variety of complex nuclei in the Universe?Describing many-nucleon systems from the fundamental theory of quantum chromodynamics has been the greatest challenge in answering this question.The chiral effective field theory description of the nuclear force now makes this possible but requires certain parameters that are not uniquely determined.Defining the nuclear force needs identification of observables sensitive to the different parametrizations.From a measurement of proton elastic scattering on 10 C at TRIUMF and ab initio nuclear reaction calculations, we show that the shape and magnitude of the measured differential cross section is strongly sensitive to the nuclear force prescription.

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)0.001
metaresearch head score (Gemma)0.002
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.072
GPT teacher head0.372
Teacher spread0.300 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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