Applicability of the Dose Spiking Electron Paramagnetic Resonance Method for the Quantitative Measurements of Low Doses in Alanine Dosimetry
Bibliographic record
Abstract
Abstract Ionizing radiation generates unpaired electrons or free radical centers in alanine. The electron paramagnetic resonance (EPR) detects, identifies, and quantifies these free radicals, proportional to the absorbed dose. The accurate measurements of low doses using EPR dosimetry with alanine are highly challenging due to (1) the weak EPR dosimetric signal from low dose alanine and measurement errors, (2) the sample anisotropy in crystalline alanine, and (3) the background signals from sample impurities. This study explores the feasibility of using the dose spiking EPR technique to overcome these challenges and decreases the detection limit up to 20 milligray (mGy) in a low dose measurement using EPR. The measurement errors from the sample anisotropy were reduced by rotating the samples relative to the constant magnetic field direction using a goniometer and averaging the resulting EPR spectra. This technique decreased the measurement errors at high doses; however, it was insufficient to decrease the detection limit and increase the measurement accuracy at low doses (<0.5 Gy). As a result, the high measurement accuracy at the high doses (>4 Gy) was exploited to increase the accuracy at the low doses using the dose spiking EPR technique. To this end, the low-dose alanine sample, undetectable and not reliably measurable in the X-band continuous wave (CW) EPR spectrometer, spikes with a high dose (4 Gy). Then, the total dose was measured and subtracted from a spike dose to get the initial low dose. This technique detected and measured the low doses with reliable accuracy (±10%). As a result, we concluded that this method has great potential to solve the low dose measurement problems in alanine dosimetry.
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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 | 0.001 |
| 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".