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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".