Quick management of accidental tritium exposure cases
Bibliographic record
Abstract
Removal half-life (RHL) of tritium is one of the best means for optimising medical treatment, reduction of committed effective dose (CED) and quick/easy handling of a large group of workers for medical treatment reference. The removal of tritium from the body depends on age, temperature, relative humidity and daily rainfall; so tritium removal rate, its follow-up and proper data analysis and recording are the best techniques for management of accidental acute tritium exposed cases. The decision of referring for medical treatment or medical intervention (MI) would be based on workers' tritium RHL history taken from their bodies at the facilities. The workers with tritium intake up to 1 ALI shall not be considered for medical treatment as it is a derived limit of annual total effective dose. The short-term MI may be considered for tritium intake of 1-10 ALI; however, if the results show intake ≥100 ALI, extended strong medical/therapeutic intervention may be recommended based on the severity of exposure for maximum CED reduction requirements and annual total effective dose limit. The methodology is very useful for pressurized heavy water reactors (PHWRs) which are mainly operated by Canada and India and future fusion reactor technologies. Proper management will optimise the cases for medical treatment and enhance public acceptance of nuclear fission and fusion reactor technologies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".