Determination of relative ion chamber calibration coefficients from depth-ionization measurements in clinical electron beams
Bibliographic record
Abstract
A method is presented to obtain ion chamber calibration coefficients relative to secondary standard reference chambers in electron beams using depth-ionization measurements. Results are obtained as a function of depth and average electron energy at depth in 4, 8, 12 and 18 MeV electron beams from the NRC Elekta Precise linac. The PTW Roos, Scanditronix NACP-02, PTW Advanced Markus and NE 2571 ion chambers are investigated. The challenges and limitations of the method are discussed. The proposed method produces useful data at shallow depths. At depths past the reference depth, small shifts in positioning or drifts in the incident beam energy affect the results, thereby providing a built-in test of incident electron energy drifts and/or chamber set-up. Polarity corrections for ion chambers as a function of average electron energy at depth agree with literature data. The proposed method produces results consistent with those obtained using the conventional calibration procedure while gaining much more information about the behavior of the ion chamber with similar data acquisition time. Measurement uncertainties in calibration coefficients obtained with this method are estimated to be less than 0.5%. These results open up the possibility of using depth-ionization measurements to yield chamber ratios which may be suitable for primary standards-level dissemination.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.002 | 0.001 |
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".