Monte Carlo calculated correction factors for primary standards of air kerma
Bibliographic record
Abstract
Many laboratories with cavity chambers as primary standards for air kerma are considering using additional Monte Carlo calculated correction factors, in particular the correction for attenuation and scatter in the walls, Kwall, and possibly the correction for point of measurement, Kan. Standards labs also use Monte Carlo calculated Spencer-Attix stopping-power ratios for graphite to air. The purpose of this article is to investigate the sensitivity of these calculations to their details and to assign uncertainties to the calculated values. We also investigate the correction needed for the Canadian primary standard to account for a polystyrene insulator, Kcomp and find that it is quite large (1.0046 +/- 0.0017). The article shows that the values of correction factors are very robust and insensitive to most details of the calculations except the values of the underlying electron stopping powers which have a significant effect on the stopping-power ratio and on Kcomp. The 1% uncertainties on the photon cross-sections have a negligible effect on these correction factors except for Kcomp. As a result of these investigations, with no change in the stopping power data used, the Canadian primary standard of air kerma in a 60Co beam needs to be increased by 0.54%.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".