Monte Carlo modeling of the response of NRC's primary beta standard
Bibliographic record
Abstract
The BEAMnrc/EGSnrc Monte Carlo code system is employed to develop a model of the National Research Council of Canada primary standard of absorbed dose to tissue in a beta radiation field, comprising an extrapolation chamber and Sr90∕Y90 beta source. We benchmark the model against the measured response of the chamber in terms of absorbed dose to air, for three different experimental setups when irradiated by the Sr90∕Y90 source. For the first setup, the chamber cavity depth is fixed at 0.2cm and the source-to-chamber distance varied between 11 and 60cm. In the other two cases, the source-to-chamber distance is fixed at 30cm. In one case the response for different chamber depths is studied, while in the other case the chamber depth is fixed at 0.2cm as different thicknesses of Mylar™ are added to the front surface of the extrapolation chamber. The agreement as a function of distance between the calculated and measured responses is within 0.37% for a variation in response of a factor of 29. In the case of dose versus chamber depth, the agreement is within 0.4% for the ISO-recommended nominal depths of 0.025-0.25cm. Agreement between calculated and measured responses is very good (between 0.02% and 0.2%) for added Mylar foils of thicknesses up to 10.8mgcm-2. For larger Mylar thicknesses, deviations of 0.6%-1.2% are observed, which are possibly due to the systematic uncertainties associated with the restricted collisional stopping powers of air or Mylar used in the calculations. We conclude that our simulation model represents the extrapolation chamber and Sr90∕Y90 source with adequate accuracy to calculate correction factors for accurate realization of dose rate to tissue at a depth of 7mgcm-2 in an ICRU tissue phantom, despite the fact that the uncertainties in the physical characteristics of the source leave some uncertainty in certain calculated quantities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".