Inmate cell fire tenability analysis
Bibliographic record
Abstract
The present project analyse the tenability of the fire scenario of a full-scale mock-up inmate cell fire that was tested in 2018 by the Correctional Services Canada (CSC) and the National Research Council (NRC). A set of test data including concentrations of various smoke effluents was obtained from the test, and the tenability analysis focuses on the incapacitating effect of smoke effluents inhaled (i.e. rather than skin-absorbed since the dermal exposure risk was investigated in 2018). A computational simulation was also conducted to understand the condition inside the test set-up and to estimate oxygen concentrations, which were not measured in the test. The simulated oxygen concentrations were also used together with the test data in the tenability analysis of the full-scale cell fire to determine the condition of the atmosphere in terms of oxygen. The analysis shows that the tenability of the inmate cell is aggravated due to the combined effects of visibility deterioration and smoke toxicity, both irritant and asphyxiant toxicity. The analysis revealed that the irritant toxic effect is more significant than the asphyxiant effect in the tested scenario. Also, carbon monoxide (CO) and hydrogen cyanide (HCN) are identified as the most significant asphyxiant in the tested scenario. The analysis calculates the time to incapacitation based on the susceptibility of 50% of the population, so vulnerable people might experience untenable conditions much earlier than the calculated end points. Also, the analysis does not consider post-exposure harm so that exposure to low effluent concentrations is potential to bring in post-exposure toxic impact. This tenability assessment helps to understand the risks of exposures to the respiratory smoke effluents during the initial response period, which include hydrogen chloride, formaldehyde, carbon monoxide, hydrogen cyanide and oxygen deficiency. For these gases, respiratory protections should be considered.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".