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Record W3205490820 · doi:10.1088/1361-6498/ac31c3

Reflection on the proposed changes to dose quantities—an industrial perspective

2021· article· en· W3205490820 on OpenAlexaff
M. Lips, E. C. Anderson, Kouji Nishida, Gerd Schneider, J. Zic, Charlotta E. Sanders, J Owen, J. Hondros, A de Ruvo

Bibliographic record

VenueJournal of Radiological Protection · 2021
Typearticle
Languageen
FieldEngineering
TopicNuclear and radioactivity studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsRadiological weaponRisk analysis (engineering)Nuclear industryRadiation protectionReflection (computer programming)Perspective (graphical)Computer scienceOccupational exposureMedical physicistBusinessMedical physicsNuclear medicineMedicineEnvironmental healthNuclear engineeringEngineering

Abstract

fetched live from OpenAlex

In 2021, the ICRP initiated the revision of the general recommendations of the system of radiation protection, and part of it will focus on dose quantities. The recently published ICRP Publication 147 and ICRU Report 95 have described the extent of the proposed modifications and paved the way for the strategy to be adopted. These revisions would seek to simplify, improve the accuracy and extend the field of use of dose quantities. While the Radiological Protection Working Group of the World Nuclear Association recognises the notable improvement in the estimation of the protection quantities and the usefulness of such changes for the medical and research sector, the benefits of the proposed new system seem very limited for the nuclear industry and industries involving naturally occurring radioactive materials. The complexity associated with changing a long-standing and robust system and the risk incurred by the human factor seem unjustified, bearing in mind the likely cost.

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)0.054
metaresearch head score (Gemma)0.074
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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.054
Threshold uncertainty score0.286

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0540.074
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.002
Science and technology studies0.0030.015
Scholarly communication0.0130.014
Open science0.0090.004
Research integrity0.0220.030
Insufficient payload (model declined to judge)0.0090.004

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 head0.147
GPT teacher head0.300
Teacher spread0.153 · 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
GenreCommentary

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

Citations1
Published2021
Admission routes1
Has abstractyes

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