Actantial Narrative Schema in Emergency Response Process Modeling for Aircraft Fires
Bibliographic record
Abstract
Analyzing the emergency response process and time characteristics after aircraft fires is extremely important for airport safety. Due to nonprocess-related elements, the Petri net model of the emergency process is generally complex and difficult to revise and expand. In this study, the actantial model was used to analyze the semantic structure of emergency actions in the Petri net emergency process model, and the actantial-timed Petri net (A-TPN) hybrid model was proposed for problem solving. The emergency response process of aircraft fires was analyzed as a case study to explain the application steps of the A-TPN hybrid model. First, the types of elements should be divided into process-related elements and nonprocess-related elements. Process-related elements include status elements and narrative elements, which are equivalent to the places and transitions in the Petri net model. Second, the workflow constructed by the status and narrative elements is converted into a Petri net. An actantial model is used to analyze the semantic structure of emergency response action narratives. Third, according to the analysis of the actants of the helper and the opponent, emergency action time characteristics are considered to construct the A-TPN hybrid model and analyze the time performance of the emergency response process. In this way, an emergency response plan can be analyzed and promoted for smart development.
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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 | 0.002 | 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 | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".