SU‐E‐T‐90: Investigation of Different Bleaching Wavelengths on the Absorbed‐Dose Sensitivity of NanoDot OSLDs Exposed to 6 MV X‐Ray Beams
Bibliographic record
Abstract
PURPOSE: :C optically stimulated luminescence detectors (OSLDs) exposed to accumulated doses of 6 MV photon beams. METHODS: OSLDs of the nanoDot type were used and readout with a microStar InLight reader. To determine the effect of different bleaching wavelengths on the luminescence response of nanoDot OSLDs, we optically reset (bleached) the OSLDs with 26 W fluorescent lamps in two modes: (i) directly under the lamps for 10, 120 and 600 min; and (ii) with a long pass filter for 55, 600 and 2400 min. The long pass filter blocks wavelengths below 495 nm, hence the longer bleaching duration to attain equivalent OSL signals as bleaching directly under the lamps. Changes in the sensitivity of the nanoDot OSLDs were determined for an irradiation-readout-bleaching-readout cycle, after irradiations with 1 and 10 Gy dose fractions. RESULTS: The nanoDot OSLDs presented a linear response to dose up to 2 Gy and supra-linear response afterwards. They produced different OSL signal loss and fading behaviors for doses of 1 and 10 Gy. OSLDs bleached for 120 min and 600 min without and with the filter in 1 Gy fractions, did not exhibit any significant change in sensitivity over an accumulated dose of 7 Gy. The OSLDs exposed with 10 Gy fractions exhibited a small under-response when bleached with filter, and an over-response when bleached directly without filter. CONCLUSIONS: The nanoDot OSLDs may be reused without a significant sensitivity change for accumulated doses below 7 Gy. However, for accumulated doses beyond 10 Gy, nanoDot OSLDs show significant sensitivity change and hence, need to be recalibrated for reuse. We also concluded that the light spectrum used to reset the OSLDs has an influence on the sensitivity of the detectors. Natural Sciences and Engineering Research Council of Canada.
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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 | 0.002 | 0.002 |
| 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 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".