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Record W4283021641 · doi:10.2172/1872378

United States Nuclear Data Program Annual Report for FY2021

2022· report· en· W4283021641 on OpenAlexfundno aff
David Brown, L. A. Bernstein, Jun Chen, John G. W. Kelley, F. G. Kondev, Hye Young Lee, E. A. McCutchan, N. Nica, M. S. Smith, I. J. Thompson

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEnvironmental Science
TopicRadioactive contamination and transfer
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
KeywordsStaffingNuclear dataFiscal yearWork (physics)Leverage (statistics)Plan (archaeology)EngineeringPolitical scienceBusinessComputer scienceNuclear physicsPhysicsFinanceGeography

Abstract

fetched live from OpenAlex

The US Nuclear Data Program (USNDP) Annual Report for Fiscal Year 2021 (FY21) summarizes the work of USNDP for the period of October 1, 2020 through September 30, 2021, with respect to the Work Plan for FY21 that was prepared in 2019. The Work Plan and Final Report for USNDP are prepared for the DOE Office of Science, Office of Nuclear Physics. The support for the nuclear data activity from sources outside the nuclear data program is described in the staffing table and in Appendix A. This leverage amounts to about 3.24 FTE scientific, to be compared with 25.095 FTEs at USNDP units funded by the DOE Office of Science, Office of Nuclear Physics. Since it is often difficult to separate accomplishments funded by various sources, some of the work reported in the present report was accomplished with nuclear data program support leveraged by other funding.

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 categoriesInsufficient payload (model declined to judge)
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 score0.274
Threshold uncertainty score0.960

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0410.000

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 head0.054
GPT teacher head0.334
Teacher spread0.280 · 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
Published2022
Admission routes1
Has abstractyes

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