Quantitative fire risk assessment of cotton storage and a criticality analysis of risk control strategies
Bibliographic record
Abstract
Summary Although fires can easily occur during cotton storage, research on cotton storage fire risk assessment is limited. This work focuses on cotton storage fire risk assessment and investigates the criticality of risk control strategies. Bow‐tie and Bayesian network models are established to investigate the relationships among accident causes, safety barriers, and possible consequences. The results show that the first safety barrier (detection and extinguishment before fire brigade arrival) is more controllable and more effective than the second safety barrier (fire brigade). Based on the collected probability data, the probability and risk of a common accident are higher than those of a large accident and severe accident when safety barriers succeed; when the first safety barrier fails, the probabilities and risks of large and severe accidents increase by more than 2000 times. The criticality of safety measures is investigated by analysing their structural importance, probability importance, and critical importance. The critical events for fire occurrence are an open flame and sparks during storage, and the critical events for detection and extinguishment before fire brigade arrival are watchkeeper monitoring, regular patrolling, and automatic fire alarm systems. For cotton storage safety, this work and its outcomes are used to support the decision‐making of fire risk prevention and control.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".