RADIOLOGICAL HAZARD ESTIMATES FROM CONTAMINATED C7 CANISTERS ON THE C4 PROTECTIVE MASK
Bibliographic record
Abstract
This study compares the external hazard posed by radioactive material trapped in the C7 filter canister of the Canadian C4 full-face mask to the internal hazard from the portion of the material that bypasses the mask and is inhaled. Published measured protection factors (PFs) are used to define the ratio of radioisotope concentration outside of the mask to that inside the mask. The hazards for a variety of radioisotopes are quantified using a Monte Carlo model for the external hazard from the contaminated canister and International Commission on Radiological Protection Publication 68 internal dose coefficients for 1 micron internalized particulate material. In general, the external hazard from a contaminated canister exceeds the internal hazard from material that bypasses the filters for only the most highly protective negative-pressure masks and then only for gamma emitting materials. Our model shows that it is highly unlikely that a canister can become contaminated with enough radioactive material to pose an immediate threat to the wearer, even for pessimistic radiological dispersal device scenarios, when the mask is being worn properly. The "as low as reasonably achievable" (ALARA) principle, however, suggests that filters should be changed as frequently as practical, and the dose measured in the filter may be useful for determining dose of record and for forensic investigations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".