SU‐E‐T‐87: The Effect of Bleaching Wavelengths on the Regeneration of the Optically Stimulated Luminescence Signal of NanoDot Dosimeters Pre‐Exposed to High‐Doses
Bibliographic record
Abstract
PURPOSE: :C nanoDot dosimeters pre-exposed to high doses. Regeneration is the increase in the OSL signal during storage of a bleached nanoDot that was previously pre-exposed to a high dose. This phenomenon affects the accuracy of a calibration protocol proposed by Jursinic 2010 (Med. Phys. 37:102) in which pre-exposure of nanoDots to a high-dose was used to minimize changes in the sensitivity of the detector as a function of accumulated dose. METHODS: :C OSLDs of the type nanoDot were used throughout this study. Readout was performed using the microStar reader. Bleaching of the OSLDs was performed with four 26 W fluorescent light bulbs in two modes: (i) directly under the lamps; and (ii) with the aid of a long-pass optical filter placed over the nanoDots, partially blocking wavelengths below 495 nm. Eighteen nanoDots were pre-exposed to 1 kGy dose. Then the pre-exposed nanoDots were bleached in two sets of 9 to very low residual OSL signals using bleaching modes (i) and (ii) for 12 h and 45 h, respectively. The nanoDots were then stored in dark and readout after various time intervals to monitor the regeneration of the OSL signal. RESULTS: We fitted the regeneration of the OSL signal using a saturation function and obtained rise-time values of 563 h and 630 h, for bleaching modes (i) and (ii), respectively. At the saturation level, the equivalent doses were about 1.18 Gy and 0.38 Gy for modes (i) and (ii), respectively. CONCLUSIONS: The regeneration rates of nanoDot OSLDs pre-exposed to high doses depend on the bleaching light wavelength used to reset the detectors. A bleaching source that has a low component of wavelengths below 495 nm can minimize the regeneration of the OSL signal. Natural Sciences and Engineering Research Council of Canada.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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 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".