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Record W2152614751 · doi:10.1093/rpd/ncp159

Comparison of codes assessing galactic cosmic radiation exposure of aircraft crew

2009· article· en· W2152614751 on OpenAlexaff
J. F. Bottollier-Depois, Peter Beck, Bryce Bennett, L. G. I. Bennett, R. Bütikofer, I. Clairand, L. Desorgher, C. S. Dyer, E. Felsberger, E. O. Flückiger, Alex Hands, Peter Kindl, Marcin Latocha, B.J. Lewis, G. Leuthold, T. Maczka, V. Mares, M. J. McCall, K. O’Brien, S. Rollet, W. Rühm, F. Wissmann

Bibliographic record

VenueRadiation Protection Dosimetry · 2009
Typearticle
Languageen
FieldMedicine
TopicRadiation Therapy and Dosimetry
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsCrewCosmic rayRadiationCOSMIC cancer databaseAeronauticsHealth threat from cosmic raysEnvironmental sciencePhysicsAstrophysicsEngineeringOpticsNuclear physics

Abstract

fetched live from OpenAlex

The assessment of the exposure to cosmic radiation onboard aircraft is one of the preoccupations of bodies responsible for radiation protection. Cosmic particle flux is significantly higher onboard aircraft than at ground level and its intensity depends on the solar activity. The dose is usually estimated using codes validated by the experimental data. In this paper, a comparison of various codes is presented, some of them are used routinely, to assess the dose received by the aircraft crew caused by the galactic cosmic radiation. Results are provided for periods close to solar maximum and minimum and for selected flights covering major commercial routes in the world. The overall agreement between the codes, particularly for those routinely used for aircraft crew dosimetry, was better than +/-20 % from the median in all but two cases. The agreement within the codes is considered to be fully satisfactory for radiation protection purposes.

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)0.003
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.028
GPT teacher head0.344
Teacher spread0.315 · 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 designSimulation or modeling
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

Citations32
Published2009
Admission routes1
Has abstractyes

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