SU-F-BRA-10: Fricke Dosimetry: Determination of the G-Value for Ir-192 Energy Based On the NRC Methodology
Bibliographic record
Abstract
Purpose: Use the methodology developed by the National Research Council Canada (NRC), for Fricke Dosimetry, to determine the G-value used at Ir-192 energies. Methods: In this study the Radiology Science Laboratory of Rio de Janeiro State University (LCR),based the G-value determination on the NRC method, using polyethylene bags. Briefly, this method consists of interpolating the G-values calculated for Co-60 and 250 kV x-rays for the average energy of Ir-192 (380 keV). As the Co-60 G-value is well described at literature, and associated with low uncertainties, it wasn't measured in this present study. The G-values for 150 kV (Effective energy of 68 keV), 250 kV (Effective energy of 132 keV)and 300 kV(Effective energy of 159 keV)were calculated using the air kerma given by a calibrated ion chamber, and making it equivalent to the absorbed to the Fricke solution, using a Monte Carlo calculated factor for this conversion. Instead of interpolations, as described by the NRC, we displayed the G-values points in a graph, and used the line equation to determine the G- value for Ir-192 (380 keV). Results: The measured G-values were 1.436 ± 0.002 µmol/J for 150 kV, 1.472 ± 0.002 µmol/J for 250 kV, 1.497 ± 0.003 µmol/J for 300 kV. The used G-value for Co-60 (1.25 MeV) was 1,613 µmol/J. The R-square of the fitted regression line among those G-value points was 0.991. Using the line equation, the calculate G-value for 380 KeV was 1.542 µmol/J. Conclusion: The Result found for Ir-192 G-value is 3,1% different (lower) from the NRC value. But it agrees with previous literature results, using different methodologies to calculate this parameter. We will continue this experiment measuring the G-value for Co-60 in order to compare with the NRC method and better understand the reasons for the found differences.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.005 | 0.002 |
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".