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Record W2165719330 · doi:10.1093/rpd/nci024

Aircrew dosimetry using the predictive code for aircrew radiation exposure (PCAIRE)

2005· article· en· W2165719330 on OpenAlexaff
B.J. Lewis, L. G. I. Bennett, A. R. Green, Anthony Butler, M. Desormeaux, F. Kitching, M. J. McCall, B. Ellaschuk, M. Pierre

Bibliographic record

VenueRadiation Protection Dosimetry · 2005
Typearticle
Languageen
FieldMedicine
TopicRadiation Therapy and Dosimetry
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsAircrewDosimetryEnvironmental scienceCosmic rayMeteorologyAltitude (triangle)RadiationPhysicsNeutronAeronauticsAerospace engineeringNuclear medicineNuclear physicsEngineeringMedicineMathematics

Abstract

fetched live from OpenAlex

During 2003, a portable instrument suite was used to conduct cosmic radiation measurements on 49 jet-altitude flights, which brings the total number of in-flight measurements by this research group to over 160 flights since 1999. From previous measurements, correlations have been developed to allow for the interpolation of the dose-equivalent rate for any global position, altitude and date. The result was a Predictive Code for Aircrew Radiation Exposure (PCAIRE), which has since been improved. This version of the PCAIRE has been validated against the integral route dose measurements made at commercial aircraft altitudes during the 49 flights. On most flights, the code gave predictions that agreed to the measured data (within +/- 25%), providing confidence in the use of PCAIRE to predict aircrew exposure to galactic cosmic radiation. An empirical correlation, based on ground-level neutron monitoring data, has also been developed for the estimation of aircrew exposure from solar energetic particle (SEP) events. This model has been used to determine the significance of SEP exposure on a theoretical jet altitude flight during GLE 42.

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)0.001
metaresearch head score (Gemma)0.004
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.026
GPT teacher head0.299
Teacher spread0.273 · 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

Citations30
Published2005
Admission routes1
Has abstractyes

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