On the characterization and uncertainty analysis of radiochromic film dosimetry
Bibliographic record
Abstract
Radiochromic film is a dosimeter of choice in applications requiring high spatial resolution, two dimensional measurements, or minimum perturbation of the beam fluence. Since the measurement uncertainty in Gafchromic film dosimetry is thought to be significant compared to that of ionization chambers, a rigorous method to evaluate measurement uncertainties is desired. This article provides a method that takes into account the correlation between fit parameters as well as single dose values in order to obtain accurate uncertainties in absolute and relative measurements. A complete portrait of all sources of uncertainty in Gafchromic film dosimetry is given. The parametrization of variance as a function of the number of averaged pixels is obtained in order to accurately predict the uncertainty as a function of the size of the region of interest. The choice of functional form for the sensitometric curve is based on four criteria and a convergence of global net optical density uncertainty to 0.0013 is demonstrated. A minimum number of 12 points is recommended to characterize the sensitometric curve to a sufficient precision on the uncertainty estimation. Uncertainty levels of 0.9% on absolute dose measurements and 0.45% on relative measurements are achieved using a 12-point calibration curve with 220 cGy and repeating measurements five times. Uncertainties of 0.8% and 0.4% are achievable when using 35 points during film characterization. Ignoring covariance terms is shown to lead to errors in the estimation of uncertainty.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".