Dosimetric modeling of the microselectron high-dose rate 192Ir source by the multigroup discrete ordinates method
Bibliographic record
Abstract
The DANTSYS multigroup discrete ordinates computer code is applied to quantitatively estimate the absorbed dose rate distributions in the vicinity of a microSelectron 192Ir high-dose-rate (HDR) source in two-dimensional cylindrical R-Z geometry. The source is modeled in a cylindrical water phantom of diameter 20 cm and height 20 cm. The results are also used for evaluation of the Task Group 43 (TG-43) dosimetric quantities. The DANTSYS accuracy is estimated by direct comparisons with corresponding Monte Carlo results. Our 210-group photon cross section library developed previously, together with angular quadratures consisting of 36 (S16) to 210 (S40) directions and associated weights per octant, are used in the DANTSYS simulations. Strong ray effects are observed but are significantly mitigated through the use of DANTSYS's stochastic ray-tracing first collision source algorithm. The DANTSYS simulations closely approximate Monte Carlo estimates of both direct dose calculations and TG-43 dosimetric quantities. The discrepancies with S20 angular quadrature (55 directions and weights per octant) or higher are shown to be less than +/- 5% (about 2.5 standard deviations of Monte Carlo calculations) everywhere except for limited regions along the Z axis of rotational symmetry, where technical limitations in the DANTSYS first collision source implementation makes adequate suppression of ray effects difficult to achieve. The efficiency of DANTSYS simulations is compared with that of the EGS4 Monte Carlo code. It is demonstrated that even with the 210-group cross section library, DANTSYS achieves two-fold efficiency gains using the the S20 quadrature set. The potential of discrete ordinates method for further efficiency improvements is also discussed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".