Poster ‐ 24: Characterization of the energy dependence of high‐sensitivity MCP‐N TLD and Al2O3:C OSLD in‐vivo dosimetry systems for 40–100 kVp energies
Bibliographic record
Abstract
Purpose: To characterize the energy dependence of high‐sensitivity MCP‐N TLD and Al2O3:C OSLD dosimetry systems at low (40–100 kVp) energies for in‐vivo dosimetry. Methods: We assessed the variation of response with energy of two detectors in the 40–100 kVp energy range: high‐sensitivity MCP‐N TLDs (LiF:Mg,Cu,P) and OSLDs (Al2O3:C). The detectors were irradiated with an XRad 320ix biological irradiator under reference conditions. The delivered dose was 10 cGy for 7 beam qualities ranging from 40–100 kVp, 1.7–4.0 mm Al, and effective energies 26.9–37.9 keV. Both sets of detectors were also irradiated under reference conditions at 6 MV using a Varian Clinac 21Ex to assess the change in response from high‐energy beams. Results: The MCP‐N high‐sensitivity TLDs were relatively insensitive to energies in the kV range, as their response varied by ±5%, i.e. well within the reproducibility limits of these detectors. However, the OSLDs exhibited a linearly‐decreasing response with energy with a response 18.7% higher at 40 kVp than at 100 kVp for the same nominal dose. Compared to the 6 MV beams used in conventional radiotherapy, OSLDs responded 3.3–3.9 times higher depending on beam quality while the MCP‐N TLD response was unchanged within experimental uncertainty. Conclusions: Unlike the more commonly used TLD‐100, the high‐sensitivity MCP‐N TLDs exhibit little to no energy response. OSLDs are shown to be highly energy‐dependent, both from MV to kV and within the kV range.
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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 | 0.001 | 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".