Methodology and Instrumentation for Electron Paramagnetic Resonance Dosimetry With Tooth Enamel
Bibliographic record
Abstract
Abstract When tooth enamel is exposed to ionizing radiation, it generates a dose-dependent concentration of free radical centers (i.e., unpaired electrons). The concentration of these free radical centers is identified and quantified using electron paramagnetic resonance (EPR) spectroscopy in the form of an EPR spectrum. The intensity of the spectrum is proportional to the absorbed dose. Four international intercomparisons have already demonstrated that the EPR tooth enamel dosimetric technique is reliable for retrospective dose assessment in acute and chronic exposure scenarios. Additionally, EPR dosimetry is regarded same as a gold standard for reconstructing the total lifetime dose of individuals using tooth enamel. The accuracy and reproducibility of EPR dose reconstruction depend on the sample preparation, spectrum acquisition, and EPR spectra analysis techniques. So, this paper reviews some of the widely applied and accepted laboratory protocols or methodologies for the EPR dosimetric methods. The minimum detection limit in tooth enamel using this technique was 30 mGy. So, this review aims to share these protocols so that it would be easy to reconstruct the accident doses or chronic exposures with reliable accuracy and precision. Different bands (e.g., L, X, Q, etc.) continuous wave (CW) EPR spectrometers have been used in many historical and accident dose reconstructions; however, due to the availability, moderate price, and not being much influenced by the small amount of moisture in a sample, the X-band has been widely used. A well-developed methodology, a highly sensitive EPR spectrometer, and a well-trained operator are vital for the reliable measurements of absorbed low doses in EPR dosimetry with tooth enamel.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".