A comprehensive system for dosimetric commissioning and Monte Carlo validation for the small animal radiation research platform
Bibliographic record
Abstract
Our group has constructed the small animal radiation research platform (SARRP) for delivering focal, kilo-voltage radiation to targets in small animals under robotic control using cone-beam CT guidance. The present work was undertaken to support the SARRP's treatment planning capabilities. We have devised a comprehensive system for characterizing the radiation dosimetry in water for the SARRP and have developed a Monte Carlo dose engine with the intent of reproducing these measured results. We find that the SARRP provides sufficient therapeutic dose rates ranging from 102 to 228 cGy min(-1) at 1 cm depth for the available set of high-precision beams ranging from 0.5 to 5 mm in size. In terms of depth-dose, the mean of the absolute percentage differences between the Monte Carlo calculations and measurement is 3.4% over the full range of sampled depths spanning 0.5-7.2 cm for the 3 and 5 mm beams. The measured and computed profiles for these beams agree well overall; of note, good agreement is observed in the profile tails. Especially for the smallest 0.5 and 1 mm beams, including a more realistic description of the effective x-ray source into the Monte Carlo model may be important.
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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 | 0.007 | 0.013 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".