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Record W4402845855 · doi:10.1051/epjconf/202430305001

Study of neutron beta decay with the Nab experiment

2024· article· en· W4402845855 on OpenAlexaff
S. Baeßler, Himal Acharya, Ricardo Alarcón, L. J. Broussard, Michael G. Bowler, David Bowman, Jin Ha Choi, Love Christie, Tim Chupp, Skylar Clymer, Christopher Crawford, G. Dodson, N. Fomin, J. R. Fry, Michael Gericke, Rebecca Godri, Francisco M. González, G. L. Greene, Andrew Hagemeier, Josh Hamblen, L. Hayen, Chelsea Hendrus, Aaron Jezghani, Huangxing Li, Nick Macsai, M. Makela, R. Mammei, David G. Mathews, August Mendelsohn, P. E. Mueller, Austin Nelsen, Jordan O’Kronley, Seppo Penttila, Jason A. Pioquinto, D. Počanić, Hitesh Rahangdale, J. Ramsey, A. Saunders, Wolfgang Schreyer, Elizabeth Mae Scott, Aryaman Singh, Leonard Tinius, A. R. Young

Bibliographic record

VenueEPJ Web of Conferences · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAtomic and Subatomic Physics Research
Canadian institutionsUniversity of WinnipegUniversity of Manitoba
Fundersnot available
KeywordsPhysicsNuclear physicsNeutronUnitarityNeutrinoBeta decayParticle physicsMeasure (data warehouse)Cabibbo–Kobayashi–Maskawa matrixComputer science

Abstract

fetched live from OpenAlex

The current three sigma tension in the unitarity test of the Cabbibo-Kobayashi-Maskawa (CKM) matrix is a notable problem with the Standard Model of elementary particle physics. A long-standing goal of the study of free neutron beta decay is to better determine the CKM element V ud through measurements of the neutron lifetime and a decay correlation parameter. The Nab collaboration intends to measure a , the neutrino-electron correlation, with accuracy sufficient for a competitive evaluation of V ud based on neutron decay data alone. This paper gives a status report and an outlook.

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)0.004
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 score0.004
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.031
GPT teacher head0.318
Teacher spread0.286 · 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
Published2024
Admission routes1
Has abstractyes

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