Poster — Thur Eve — 32: Water tank referenced calibration method for detector array devices
Bibliographic record
Abstract
INTRODUCTION: Detector array devices, such as the I'mRT Matrixx (IBA Dosimetry), provide a means of evaluating beam profiles with respect to gantry for a range of dose rates and monitor units. The relative calibration of these devices is typically highly susceptible to even relatively small variations in beam output. An alternative method is proposed here, which directly references the device detector response to water tank data. METHODS: The Matrixx response was measured at the four cardinal angles for three devices. A calibration factor was determined for each orientation of the Matrixx device by dividing a water tank measured profile by the Matrixx response for the in-plane and cross-plane detectors. A geometric mean of each orientation was used as the estimate of the calibration coefficient. RESULTS: Before calibration, the three-detector average of the deviation from the profile measured in the water tank centered on each of the horns was 0.4% (SD 0.2%); applying the calibration procedure reduced this to 0.1% (SD 0.1%). The energy independence of the proposed relative calibration was also confirmed. A comparison of the linac output for relatively short Matrixx acquisitions to the longer water tank acquisition suggested some difference. This difference was mitigated by averaging. CONCLUSIONS: The proposed water tank reference calibration procedure is an effective means of determining the relative calibration of a detector array and mitigates the effect of compound error by avoiding the recursive algorithm of typical calibration methods. In addition it has the benefit of being directly relatable to commissioning beam data.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".