Sci—Fri PM: Delivery — 06: A generalized solution to the wide field array calibration method
Bibliographic record
Abstract
Multi detector arrays are commonly used in radiation oncology for IMRT and Linac QA. The calibration of detector arrays is usually based on the wide field calibration technique. Unfortunately small beam changes between measurements will result in large error propagation. The present work introduces a generalized modified version of the wide field calibration method, robust against measurement to measurement variation. Our generalized framework uses an unlimited number of measurement pairs, n geometric positions providing n(n-1)/2 pairs. We solve this large over determined linear system using least squares with gradient method. Measurements were made on an Elekta synergy 6 MV beam with two IBA Matrixx detectors, each containing a 32 × 32 array (1024) of vented pixel ionization chambers. Data acquisition was by the IBA Omnipro Advance software, version 1.2 running in the "ONLINE" cine mode with a 10 sec integration time. Continuous beam sampling (10 seconds long) measured over 10 minutes demonstrated why consistent calibration using the conventional wide field calibration is a challenge. Overall signal changes of 1.6%, flatness changes of 0.3% and the beam symmetry changes of 0.2% over the full 10 minute beam-on time were observed. For the purpose of testing and demonstration of our method, we have chosen to make measurements in 5 geometric orientations relative to the beam, including 1 reference position, 2 rotations and 2 translations. With our method we were able to calibrate all 1024 detectors to better than 0.6% total uncertainty as demonstrated against inter and intra MatriXX comparison.
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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 | 0.004 |
| 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 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".