Assessing the effectiveness and the economic impact of evacuation: the case of Vulcano Island, Italy
Bibliographic record
Abstract
Abstract. Evacuation planning and management represents a key aspect of volcanic crises because it can increase people protection as well as minimize the potential impact on the economy, properties, and infrastructure of the affected area. Assessment of evacuation scenarios that consider human and economic impact is best done in a pre-disaster context as it helps authorities develop evacuation plans and make informed decisions outside the highly stressful time period that characterizes crises. We present an agent-based simulation tool that assesses the effectiveness of different evacuation scenarios using Vulcano island (Italy) as a case study. Simulation results show that the overall time needed to evacuate people should be analysed together with the percentage of people evacuated as a function of time and that a simultaneous evacuation on Vulcano is more efficient than a staged evacuation. We also present a model to assess the economic impact of evacuation as a function of evacuation duration and starting period that reveals that an evacuation of Vulcano would cause significant economic impact to the tourism industry if lasting more than 3 months (in case it was initiated at the beginning of the visitor season) to 1 year (in case it was initiated at the end of the visitor season).
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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 | 0.001 | 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".