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Record W4367669688 · doi:10.2172/1971782

United States Nuclear Data Program Annual Report (FY 2022)

2023· report· en· W4367669688 on OpenAlexfundno aff
David Brown, L. Bernstein, Jinkun Chen, J. Kelley, F. Kondev, Hyukjun Lee, E. McCutchan, N. Nica, M. Smith, I. Thompson

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEngineering
TopicNuclear and radioactivity studies
Canadian institutionsnot available
FundersLawrence Berkeley National LaboratoryOak Ridge National LaboratoryBrookhaven National LaboratoryU.S. Department of EnergyArgonne National LaboratoryLawrence Livermore National LaboratoryNuclear PhysicsMcMaster UniversityNational Institute of Standards and TechnologyCollege of Engineering, Michigan State UniversityOffice of ScienceNorth Carolina State UniversityLos Alamos National LaboratoryNational Nuclear Security AdministrationMichigan State University
KeywordsNuclear dataWork (physics)Nuclear engineeringPolitical scienceEngineeringEnvironmental scienceNuclear physicsPhysicsMechanical engineering

Abstract

fetched live from OpenAlex

This document describes the activities including related metrics performed by the US Nuclear Data Program members during Fiscal Year 2022.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.222
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.090
GPT teacher head0.345
Teacher spread0.256 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2023
Admission routes1
Has abstractyes

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