Sci‐Fri AM General‐03: Variation in the relative volumes and ionization response of four cylindrical ion chambers using micro‐computed tomography
Bibliographic record
Abstract
High accuracy in absolute dose measurements is required in radiation therapy and dosimetric protocols require the use of air ionization chambers. These protocols involve chamber specific calibration factors, which depend fundamentally upon the chamber volume. In this work, we investigate the ability of micro‐computed tomography (micro‐CT) to determine differences in the chamber volume of four nominally identical Exradin A1SL cylindrical ion chambers. A GE Locus micro‐CT imaging system (General Electric Healthcare, London, Ontario) was used to acquire 20 micron resolution images of the four ion chambers. The ionization response of each chamber in a 10×10cm2 10 MV x‐ray reference field was also measured, and the readings were corrected for polarity and ion recombination effects. GE MicroView software (Version 2.1.1) and its semi‐automatic pixel volume measurement option was used to determine the sensitive air volume of the ion chambers. Both the ionization response of the chambers and the volumes were normalized to the chamber with the largest values. The relative differences in response and volume for each chamber agreed within 1%. Micro‐CT is a promising tool for the accurate determination of chamber volume, and potentially for the independent determination of chamber calibration factors.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".