WE‐G‐BRB‐05: On the Importance of Fluorescence Within the Stem Effect of Scintillation Detectors
Bibliographic record
Abstract
PURPOSE: To quantify the nature and composition of the light produced in optical fibers under different irradiation conditions and evaluate its impact on dosimetry. METHODS: Irradiation of a bare PMMA optical fiber (Mitsubishi ESKA Premier) was performed using a superficial therapy unit, an Ir-192 HDR brachytherapy source, a Co-60 external-beam unit as well as photon and electron beams from a linear accelerator. Spectra of the radiation-induced visible light in the fiber were acquired and signals were compared as a function of depth and irradiation type. Irradiation of a 75 kVp beam from the superficial therapy unit was used to isolate the fluorescence spectrum. Isolation of the Cerenkov spectrum component was obtained from irradiation of a 15 MeV electron beam at a 45 degree angle. Relative composition in fluorescence and Cerenkov of the stem effect light has been determined for all irradiations. RESULTS: The total stem effect spectra can be represented by a linear superposition of the fluorescence and Cerenkov spectra. The fluorescence contribution was shown to strongly differ between the superficial therapy unit (99%±1%), the Ir-192 HDR source (25%±3%) and higher energy irradiations (3%±2%). Variations within each energy regime (kV, HDR brachytherapy and MV) were small at 3% or lower. These were observed for irradiations at angle or when the fiber was near the surface. This study suggests it is better to calibrate the stem effect of a scintillation detector using the same irradiation modality. CONCLUSIONS: Stem effect light was shown to be composed of fluorescence and Cerenkov light in different proportions depending on the geometry of the experimental setup, nature of the irradiation, and irradiation energy. Calibrating detectors separately for fluorescence and Cerenkov may lead to better performance of the stem effect removal technique.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".