Monte Carlo simulations of semi-infinite clouds of radioactive noble gases
Bibliographic record
Abstract
Health Canada maintains detector networks across Canada. One of these networks consists of NaI(Tl) detectors that measure air KERMA [1]. Located beside the NaI(Tl) detector in Ottawa is a radioxenon analyzer [2] that measures the activity concentration of 131m, 133m, 133, 135Xe directly. The ICRU-accepted KERMA to activity concentration conversion factor for 133Xe, for a semi-infinite cloud measured 1 m off the ground, is 9.68 pGy/hr per Bq/m3 [3]. However, on various dates, the two detectors in Ottawa reported a conversion value of 2.6 ± 0.2 pGy/hr per Bq/m3; we have resolved this discrepancy [4] and have expanded on the study of other isotopes by focusing on the NaI(Tl) detector. Greater accuracy in the conversion value between the air KERMA and activity concentration will assist meteorological modellers in verifying their models [1]. Two Monte Carlo methods were used in this investigation. The first is the analogue geometry where the detector is immersed in a semi-infinite radioactive source. A second method is to apply a reciprocal transform of the analogue geometry. This expedited the calculation of larger clouds. By using these two methods together, we have calculated new values for KERMA rate to activity concentration for 4 of isotopes of noble gases, namely 131mXe, 133mXe, 85Kr, 85mKr, 135Xe.
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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 | 0.005 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".