TH-C-T-617-08: Monte Carlo Modelling of the Response of NRC's 90Sr/90Y Primary Beta Standard
Bibliographic record
Abstract
Purpose: To benchmark an EGSnrc Monte Carlo calculated response against the high quality measured response of an extrapolation chamber used as NRC's primary standard of absorbed dose to tissue in a beta field. Method and Materials: The BEAMnrc code was used to model the NRC's beta source and indigenously developed extrapolation chamber. The calculated response was compared to the measured response in 3 different series of measurements. An overall scale factor was determined by a global fit. It was used to scale the calculated values to the measured values and was compared to the known activity of the source. A single measurement configuration (30 cm distance, 0.2015 cm chamber depth) was common to all 3 sets of experimental data. Results: The scale factor led to an estimated source activity of 1.237±0.08% GBq which is consistent with the nominal value of 1.2±0.1 GBq. As the source-detector distance was varied from 11 cm to 60 cm, values of calculated and measured responses agreed within 0.37% for a variation in response by a factor of 29. As chamber depth was varied from 0.05 cm to 0.25 cm the values agreed within 0.4%. As Mylar thicknesses up to 11 mg/cm2 were added to the face of the chamber, the values agreed within 0.2%, and agreed within 1.2% up to 150 mg/cm2. Conclusion: This project demonstrates EGSnrc's ability to calculate the response of extrapolation chamber with a remarkable degree of accuracy. Such high precision comparisons with experimental data are rare. This benchmarking of the Monte Carlo model will allow it to be used to calculate correction factors needed for the NRC's primary standard.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".