A New Software for the Calculation of Eye-lens Dosimetry Quantities Based on Dose-rate Coefficients
Bibliographic record
Abstract
ABSTRACT: In the last decade, the International Commission on Radiological Protection recommended a reduction in the annual limits to the dose to the lens of the eye from 150 mSv to 20 mSv y -1 , averaged over defined periods of 5 y, with no single year exceeding 50 mSv. To assist the health physics community in this task, many groups have calculated protection and operational fluence dose coefficients. This led to the publication of multiple coefficient tables that were calculated for arrays of different parameters, including particle type, angle of incidence, target phantom models, presence or absence of secondary charged particle equilibrium, etc. The coefficients available in the literature include protection dose values calculated in a realistic eye model and operational values calculated in a simplified cylindrical head phantom at a point 3 mm below the surface. This paper reports on a simple Windows™ application that was written to aid health physics professionals in accessing and using the large body of available protection and operational eye-lens data. The application is called the Eye-Lens Dose Calculator, as it also performs calculations of the eye-lens dose for radionuclides, where the complete emissions of the selected radionuclides are considered. Test cases show that there is good agreement between the calculated protection and operational dose quantities when radionuclide emission characteristics are considered.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 0.011 |
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 source (direct Gemma or distilled Codex), 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".