On the characterization and uncertainty analysis of radiochromic film dosimetry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it