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Record W2080599242 · doi:10.1080/10506890701750418

Mass Chain Evaluations for the Evaluated Nuclear Structure Data File (ENSDF)—An Urgent Appeal for European Participation

2007· article· en· W2080599242 on OpenAlexfundno aff
F. G. Kondev, Alan L. Nichols, J.K. Tuli

Bibliographic record

VenueNuclear Physics News · 2007
Typearticle
Languageen
FieldEngineering
TopicNuclear reactor physics and engineering
Canadian institutionsnot available
FundersBrookhaven National LaboratoryNuclear PhysicsTRIUMF
KeywordsAppealPolitical scienceLaw

Abstract

fetched live from OpenAlex

Reliable nuclear structure and decay data represent the fundamental building blocks of nuclear physics and astrophysics research, and are also of vital importance in a significant number of applied nuclear fields such as power generation and associated fuel cycle operations (e.g., fuel manufacture, transport, reprocessing, and waste management), materials analysis, medical diagnosis, and radiotherapy. There is a continuous demand for good quality data formulated and recommended through the speedy assessment and incorporation of new and improved 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)0.067
metaresearch head score (Gemma)0.120
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.067
Threshold uncertainty score0.353

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0670.120
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0050.007
Science and technology studies0.0020.001
Scholarly communication0.0070.012
Open science0.0050.005
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0360.013

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 head0.062
GPT teacher head0.306
Teacher spread0.243 · 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 designNot applicable
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

Citations4
Published2007
Admission routes1
Has abstractyes

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