Modeling impacts of combustion products on humans in complex processing facilities
Bibliographic record
Abstract
Abstract Heat radiation, combustion products, and flame engulfment are the main hazards from a fire event. Several reports have indicated that fire toxicity has caused greater numbers of human injuries and fatalities than those of heat radiation and flame engulfment. In a complex facility, the risk of fire toxicity increases significantly due to the difficulty of escaping in cases of emergency rescue and evacuation operations. This study proposes a methodology to model the impact of combustion products on humans. Several fire scenarios were considered to identify credible fire scenarios. In the most credible fire scenario, concentrations of combustion products over the layout of a facility were modeled. The results showed that the concentrations of CO and CO 2 are below the short‐term exposure limit and lethal concentration 50 threshold limits at the considered time. The possible reasons for this are that the fire location was in a well‐ventilated area and equipment layouts were designed with proper safety gaps replicating a full‐scale dimension of an operating floating liquefied natural gas processing facility. The concentration of soot is above the threshold limit, and this has the potential to cause adverse health effects. This study can provide a tool for assessing fire toxicity in processing facilities, which will help improve safety measures.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".