A Practical Method for EPR Dosimetry Using Alanine Powder
Bibliographic record
Abstract
ABSTRACT: This work investigates alanine powder, an inexpensive and versatile material compared to alanine pellets, as a standardized dosimeter for the alanine-EPR system using a Bruker EMX-Micro spectrometer. The feasibility of this method was investigated, and a calibration curve was produced using 40 dosimeters, which were prepared by tightly packing DL-alanine powder in polypropylene microcentrifuge tubes. The dosimeters were irradiated to doses ranging from 0.2-20 Gy using a 60 Co source. A dosimeter handling and measurement protocol was established for all dosimeters. The dosimetric signal was evaluated by measuring the peak-to-peak height of the central resonance peak, and the dose response of alanine powder dosimeters showed a linear behavior in the investigated dose range with relative errors below 13%. Measurement repeatability and reproducibility were tested to show the errors associated with sample placement in the cavity and with the overall measurement method, with both tests showing relative errors below 7%. As an inexpensive material compared to pellet dosimeters, alanine powder has a strong potential to be used as a standardized material for radiation dosimetry applications. The scope of this work is to present an effective and comprehensive methodology with accompanying analysis scripts for dosimetry with alanine powder that is useful in a wide range of applications and dose requirements.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".