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Record W4285065623 · doi:10.1088/0026-1394/59/1a/06012

Supplementary comparison CCRI(I)-S3 of standards for absorbed dose to water in <sup>60</sup>Co gamma radiation at radiation processing dose levels

2022· article· en· W4285065623 on OpenAlexaff
M. R. McEwen, P. H.G. Sharpe, I. M. Pazos, A. Miller, E. Pawlak, S. Ninlaphruk, Y. Zhang, C Kessler

Bibliographic record

VenueMetrologia · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRadiation Effects and Dosimetry
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsMutual recognitionDosimeterNISTAbsorbed doseDose rateRadiochemistryNuclear medicineMathematicsRadiationMedical physicsPhysicsMedicineNuclear physicsChemistryComputer science

Abstract

fetched live from OpenAlex

Main text A supplementary comparison of standards for absorbed dose to water in 60 Co fields used for calibrations at radiation processing dose levels has been completed. Alanine dosimeters from the NIST and the NPL were used as transfer dosimeters and all participant irradiations were carried out in self-shielded irradiators. Irradiations were also carried out at the BIPM to allow direct comparison of dose ratios with the Key Comparison BIPM.RI(I)-K4. No significant difference was seen between the dose ratios obtained using the NPL and NIST alanine systems, no significant impact of mailing on dosimeter response was noted, and the adopted protocol limited any dose rate effects to the level of Type A uncertainties. The national standards of the participants are in agreement within the standard uncertainties, which are in the range from 1 to 2 parts in 10 2 . To reach the main text of this paper, click on Final Report . Note that this text is that which appears in Appendix B of the BIPM key comparison database https://www.bipm.org/kcdb/ . The final report has been peer-reviewed and approved for publication by the CCRI, according to the provisions of the CIPM Mutual Recognition Arrangement (CIPM MRA).

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)0.002
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.465
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.297
Teacher spread0.272 · 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 designObservational
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

Citations2
Published2022
Admission routes1
Has abstractyes

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