Activities of ISO Working Group 18: Biological and Physical Retrospective Dosimetry
Bibliographic record
Abstract
Biological and physical retrospective dosimetry for ionizing radiation exposure is a rapidly growing field, and several methods for performing biological and physical retrospective dosimetry have been developed to provide absorbed dose estimates for individuals after occupational, accidental, intentional, and incidental exposures to ionizing radiation. In large-scale radiological/nuclear incidents, multiple retrospective dosimetry laboratories from several countries may be involved in providing timely dose estimates for effective medical management of several thousand exposed individuals. In such scenarios, the harmonization of methods among participating laboratories is crucial for consistency in data analysis, dose estimation, and medical decision-making. In this regard, ISO documents ensure that these practices are standardized globally across the laboratories by providing quality assurance and quality control documentation that guide laboratories in maintaining high-quality performance for consistency. With the intent of bringing standardization and harmonization of biological and physical retrospective dosimetry methodologies across national and international laboratories, the ISO working group 18 (WG18) was established under ISO/TC85/SC2 (Technical Committee 85, Subcommittee 2-Radiation Protection) in 1999. This manuscript summarizes some of the past, current, and future activities of WG18 on biological and physical retrospective 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 | 0.059 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.008 |
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".