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Record W2041506679 · doi:10.1524/ract.2012.1959

Radioactive decay data: powerful aids in medical diagnosis and therapy, analytical science and other applications

2012· article· en· W2041506679 on OpenAlex
Alan L. Nichols

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueRadiochimica Acta · 2012
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNuclear Physics and Applications
Canadian institutionsnot available
FundersInternational Atomic Energy AgencyArgonne National LaboratoryForschungszentrum JülichMcMaster University
KeywordsRadioactive decayNuclear dataExperimental dataDecay energyChemistryNuclear physicsDecay schemePhysicsStatisticsGamma rayNeutron

Abstract

fetched live from OpenAlex

Abstract Decay data are commonly used to characterise and quantify radioactive material, and provide an important means of understanding the properties and structure of the nucleus. Experimental measurement techniques are reviewed, with the emphasis placed on recent developments that represent a potential quantum leap in advancing our knowledge, particularly by means of γ-ray spectroscopy. A select number of internationally-accepted decay-data evaluations and compilations are also discussed in terms of their contents. Both energy and non-energy related applications require the input of well-defined decay data, and such activities have been reviewed. Various important decay-data issues are assessed, and note taken of any significant requirements for better quantified data.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.801
Threshold uncertainty score0.440

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 science0.0000.000
Research integrity0.0000.000
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 head0.025
GPT teacher head0.314
Teacher spread0.288 · 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