SU‐DD‐A4‐03: Radiological Low Dose Measurement with An OSL Dosimetric System
Bibliographic record
Abstract
Purpose: A method has been developed to measure low dose in radiology using the Landauer microStar OSL reader and microdot dosimeters. The depletion rate (the fraction of trapped electrons participating in the formation of the signal in a reading) has been established and the noise behavior following consecutive readings modeled. Method and Materials: While microdot dosimeters can be used to measure dose levels as low as 10 μGy, caution must be used as repeated expositions of these dose integrators rapidly limits the accuracy of the readings. Around 4000 doses were measured with a set of 362 dosimeters, each dosimeter being reused after reading. Each dosimeter was read multiple times, and a bank of nearly 70000 measurements was acquired. In order to obtain exposition dose, a method taking the multiple readings of the dosimeter into account was devised to estimate accumulated dose before and after exposition. The difference between these two values was the estimated exposition dose. It was found that low doses were more accurately measured when dosimeter accumulated dose was kept below 1500 μGy. This was achieved by exposing the microdot dosimeters to tungsten light for twelve hours. The dosimeter accumulated dose was reset to 30 μGy, without affecting significantly the dosimeter operating characteristics. Results: We found that the relation between the noise variance and the accumulated dose is quadratic for doses between 50 μGy and 10 mGy in the case of 90–140 kV exposure. Noise variance dictates that 200 readings be done in order to estimate the dosimeter rate of depletion with sufficient accuracy. The rate of depletion for the microdot dosimeter is −0.29% ± 0.03 (2 standard deviations). Conclusion: OSL allows measuring 10 μGy doses with an error of ± 3 μGy, but only with multiple readings after exposition and light induced resetting to zero.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".