Bibliographic record
Abstract
Purpose: To improve the accuracy of the MOSFET calibration procedure by performing the measurement several times and calculating the average value of the calibration factor for various photon and electron energies. Methods: The output of three photon and six electron beams of Varian Trilogy linear accelerator SN 5878 was calibrated. Five reinforced standard sensitivity MOSFET dosimeters were placed in the calibration jig and connected to the Reader Module. As the backscatter material was used 7 cm of Virtual Water. The MOSFET dosimeters were covered with 1.5 cm thick bolus for the regular and SRS 6 MV beams, 3 cm bolus for 15 MV beam, 1.5 cm bolus for 6 MeV electron beam, and 2 cm bolus for the electron energies of 9, 12, 15, 18, and 22 MeV. The dosimeters were exposed to 100 MU, and the calibration factor was determined using the mobileMOSFET software. To improve the accuracy of calibration, this procedure was repeated ten times and the calibration factors were averaged. Results: As the number of calibrations was increasing the variability of calibration factors of different dosimeters was decreasing. After ten calibrations, the calibration factors for all five dosimeters were within 1% of one another for all energies, except 6 MV SRS photons and 6 MeV electrons, for which the variability was 2%. Conclusions: The described process results in calibration factors which are almost independent of modality or energy. Once calibrated, the dosimeters may be used for in‐vivo dosimetry or for daily verification of the beam output. Measurement of the radiation dose under bolus and scatter to the eye are examples of frequent use of calibrated MOSFET dosimeters. The calibration factor determined for full build‐up is used under these circumstances. To the best of our knowledge, such thorough procedure for calibrating MOSFET dosimeters has not been reported previously. Best Medical Canada provided MOSFET dosimeters for this project.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 0.002 |
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".