SU-E-T-106: Experimental Characterization of Al2O3:C Optically Stimulated Luminescence Detector (OSLD) Exposed to 6 MV X-Ray Beams
Bibliographic record
Abstract
Purposes: To characterize the response of Al2O3:C optically stimulated luminescence detectors (OSLDs) to 6 MV x-ray beams and to determine the optimal bleaching method that would allow the re-use of the OSLDs. Methods: OSLDs were exposed to a 6 MV x-ray linac beam in the dose range from 50 mGy–10 Gy. Readouts were performed with a commercial OSLD reader (microStarTM, Landauer Inc.). For 50 mGy and 100 mGy doses, OSLDs were readout using the readerˈs low-dose mode, which provides higher stimulation power compared to the high-dose mode. All other readouts were performed using the high-dose mode. The OSLD response to dose, bleaching time and repeated readouts (depletion) were determined. Bleaching of the OSL signal was performed using a 250 W halogen lamp with two Methods: (a) direct exposure to light; and (b) exposure using a long-pass optical filter to block wavelengths shorter than 495 nm. Results: The OSLD dose-response was linear for the investigated dose range. After 100 readouts, the OSL signal was depleted by (24.5 ± 0.7) % and (3.16 ± 0.07) % with a depletion rate of (0.251 ± 0.002) and (0.023 ± 0.002) %/readout for low- and high-dose modes, respectively. After a 5 min bleaching time, (85.1 ± 1.4) and (70.5 ± 2.0) % reductions in the OSL signal for all doses was attained using methods (a) and (b), respectively. After a 100 min bleaching time, (99.5 ± 0.2) % reduction was attained. Conclusions: We observed linearity of the OSLDˈs dose response for the investigated dose range. A 100 min bleaching time was sufficient to bleach 99.5 % of the OSL signal for both bleaching methods. The depletion rate using the low-dose mode is 11 times higher than using the high-dose mode. for the high-dose mode of the reader, the depletion rate is independent on dose.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".