Dose calibration optimization and error propagation in polymer gel dosimetry
Bibliographic record
Abstract
This study reports on the relative precision, relative error, and dose differences observed when using a new full-image calibration technique in NIPAM-based x-ray CT polymer gel dosimetry. The effects of calibration parameters (e.g. gradient thresholding, dose bin size, calibration fit function, and spatial remeshing) on subsequent errors in calibrated gel images are reported. It is found that gradient thresholding, dose bin size, and fit function all play a primary role in affecting errors in calibrated images. Spatial remeshing induces minimal reductions or increases in errors in calibrated images. This study also reports on a full error propagation throughout the CT gel image pre-processing and calibration procedure thus giving, for the first time, a realistic view of the errors incurred in calibrated CT polymer gel dosimetry. While the work is based on CT polymer gel dosimetry, the formalism is valid for and easily extended to MRI or optical CT dosimetry protocols. Hence, the procedures developed within the work are generally applicable to calibration of polymer gel dosimeters.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".