A NEW SEMI-EMPIRICAL AMBIENT TO EFFECTIVE DOSE CONVERSION MODEL FOR THE PREDICTIVE CODE FOR AIRCREW RADIATION EXPOSURE (PCAIRE)
Bibliographic record
Abstract
The Predictive Code for Aircrew Radiation Exposure (PCAIRE) is a semi-empirical code that estimates both ambient dose equivalent, based on years of on-board measurements, and effective dose to aircrew. Currently, PCAIRE estimates effective dose by converting the ambient dose equivalent to effective dose (E/H) using a model that is based on radiation transport calculations and on the radiation weighting factors recommended in International Commission on Radiological Protection (ICRP) 60. In this study, a new semi-empirical E/H model is proposed to replace the existing transport calculation models. The new model is based on flight data measured using a tissue-equivalent proportional counter (TEPC). The measured flight TEPC data are separated into a low- and a high-lineal-energy spectrum using an amplitude-weighted 137Cs TEPC spectrum. The high-lineal-energy spectrum is determined by subtracting the low-lineal-energy spectrum from the measured flight TEPC spectrum. With knowledge of E/H for the low- and high-lineal-energy spectra, the total E/H is estimated for a given flight altitude and geographic location. The semi-empirical E/H model also uses new radiation weighting factors to align the model with the most recent ICRP 103 recommendations. The ICRP 103-based semi-empirical effective dose model predicts that there is a ∼30 % reduction in dose in comparison with the ICRP 60-based model. Furthermore, the ambient dose equivalent is now a more conservative dose estimate for jet aircraft altitudes in the range of 7–13 km (FL230–430). This new semi-empirical E/H model is validated against E/H predicted from a Monte Carlo N-Particle transport code simulation of cosmic ray propagation through the Earth's atmosphere. Its implementation allows PCAIRE to provide an accurate semi-empirical estimate of the effective dose.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".