A fire management decision support systems to minimise economic losses: a case study in a petrochemical complex
Bibliographic record
Abstract
Fires are very expensive to fight and may result in devastating human, economic, and environmental effects. Due to limited fire management resources and budget constraints, fire management becomes increasingly challenging. The increased interdependencies among existing infrastructure systems make economic losses induced by fires very severe and difficult to predict. Despite recent advances in fire management decision support systems (FMDSSs), economic analysis capabilities have not received enough attention in these systems. Efficient FMDSS incorporates economic considerations to determine optimal fire fighting tactics and strategies. This paper proposes an FMDSS for developing optimal fire management plans. The proposed system adopts the cost-plus-net-value change (C + NVC) concept to evaluate the economic efficiency of the plans. In order to capture the net value change of goods and services due to fires, an infrastructure interdependency simulator (i2Sim) is used to incorporate the interaction among infrastructure systems. The proposed FMDSS is capable of developing long-term (strategic) plans and short-term (operational) plans. The applicability of the proposed system is demonstrated using a case study involving multiple fire incidents in a large petrochemical complex.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".