Bibliographic record
Abstract
Tritiated particulate materials are of potential hazard in fission, fusion, and other tritium handling facilities. The absorbed fractions (fraction of energy emitted that is absorbed by the target region) are calculated for tritiated particles deposited in the alveolar-interstitial (AI) region of the respiratory tract. The energy absorbed by radiologically sensitive tissue irradiated by tritiated particles, in regions of the lung other than in the AI region, is negligible. The ICRP Publication 71 assumes the absorbed fraction is unity for tritium deposited in the AI region. We employed Monte Carlo methods in a model to evaluate the energy deposition in the wall of the alveolar sac from particles of tritiated beryllium, tritiated graphite, titanium tritide, tritiated iron hydroxide and zirconium tritide. For the five materials examined, the absorbed fraction in alveolar tissue ranged from 0.31 to 0.61 for particles of 1 microm physical diameter and 0.07 to 0.21 for 5 microm diameter particles. The dose to alveolar tissue, for an acute inhalation of tritiated particles by an adult male worker, was calculated based on the ICRP 66 lung model and the particle dissolution model of Mercer (1967). For particles of 5 microm activity median aerodynamic diameter (AMAD), the committed equivalent dose to alveolar tissue, calculated for the five materials, ranged from 32-42%, respectively, of the committed equivalent dose derived assuming the absorbed fractions were unity.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".