Emergency Response Plan Modeling Using IDEF0 and BPMN Approaches
Bibliographic record
Abstract
Emergency response plans play a key role in limiting the consequences of major accidents and consequently preventing them from causing domino effects.It is therefore crucial to efficiently design and implement emergency response plans according to the expected accidents.Within this framework, this paper is aiming to present a structured approach in order to model and evaluate the performance of such plans, based on IDEF0 and BPMN (Business Process Modeling Notation) methods.In fact, the IDEF0 allow a detailed functional and structural description of the emergency response plan, whereas the BPMN is used to clarify the relations between its different components and to simulate it.The simulation results give valuable information regarding the execution of the emergency response process, especially the required time to reach a safe situation.The proposed approach was illustrated on a special emergency plan called "Internal Intervention Plan: IIP" related to a gasoline storage leakage that may lead to a major accident scenario (fire) within an LNG facility.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".