MONTE CARLO-BASED SPENCER–ATTIX AND BRAGG–GRAY TISSUE-TO-AIR STOPPING POWER RATIOS FOR ISO BETA SOURCES
Bibliographic record
Abstract
Spencer-Attix (SA) and Bragg-Gray (BG) mass-collision-stopping-power ratios of tissue-to-air are calculated using a modified version of EGSnrc-based SPRRZnrc user-code for the International Organization for Standardization (ISO) beta sources such as (147)Pm, (85)Kr, (90)Sr/(90)Y and (106)Ru/(106)Rh. The ratios are calculated at 5 and 70 µm depths along the central axis of the unit density ICRU-4-element tissue phantom as a function of air-cavity lengths of the extrapolation chamber l = 0.025-0.25 cm. The study shows that the BG values are independent of l and agree well with the ISO-reported values for the above sources. The overall variation in the SA values is ∼0.3% for all the investigated sources, when l is varied from 0.025 to 0.25 cm. As energy of the beta increases the SA stopping-power ratio for a given cavity length decreases. For example, SA values of (147)Pm are higher by ∼2% when compared with the corresponding values of (106)Ru/(106)Rh source. SA stopping-power ratios are higher than the BG stopping-power ratios and the degree of variation depends on type of source and the value of l. For example, the difference is up to 0.7 % at l = 0.025 cm for the (90)Sr/(90)Y source.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".