Sci‐Fri PM: Planning‐10: The replacement correction factors for cylindrical chambers in megavoltage beams
Bibliographic record
Abstract
The replacement correction factor (Prepl) in ion chamber dosimetry accounts for the effects of the medium being replaced by the air cavity of the chamber. In TG‐21, Prepl was conceptually separated into two components: fluence correction, Pfl, and gradient correction, Pgr. In TG‐51, for electron beams, the calibration is at dref where Pgr is required for cylindrical chambers and Pfl is unknown and assumed to be the same as that for a beam having the same mean electron energy at dmax. For cylindrical chambers in high‐energy photon beams, Prepl also represents a major uncertainty in current dosimetry protocols. In this study, Prepl is calculated with high precision (<0.1%) by the Monte Carlo method as the ratio of the dose in a phantom to the dose scored in water‐walled cylindrical cavities of various radii (with the center of the cavity being the point of measurement) in both high energy photon and electron beams. It is found that, for electron beams, the mean electron energy at depth is a good beam quality specifier for Pfl; and TG‐51's adoption of Pfl at dmax with the same mean electron energy for use at dref is proven to be accurate. For Farmer chambers in photon beams, there is essentially no beam quality dependence for Prepl values. In a Co photon beam, the calculated Prepl is about 0.4–0.6% higher than the TG‐21 value, indicating TG‐21 (and TG‐51) used incorrect values of Prepl for cylindrical chambers.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 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".