Evacuating isolated islands with marine resources: A Bowen Island case study
Bibliographic record
Abstract
Inhabited islands are susceptible to natural hazards, such as wildfires. To avoid disasters, preventative measures and guidelines need to be in place to strengthen community resilience. If these fail, evacuation is often the only choice. However, island evacuation is a vastly understudied problem in both research and practice, particularly for islands without permanent road connections to the mainland that require marine evacuation. Multiple vessel trips are necessary to evacuate the population from suitable access points, which previous studies did not entertain. Furthermore, most existing studies either focus on evacuations from an academic, or from a government perspective. Instead, this paper presents a collaborative approach. It applies a recently developed evacuation routing model that optimizes the evacuation plan for Bowen Island in Canada through minimizing the expected evacuation time across disaster scenarios. These were designed with the participation of a broad range of stakeholders, from local residents and volunteer groups to agencies from all levels of government and companies, which integrates both academic and practical perspectives to maximize solution quality. Different options for fleet sizes, staging locations and scenarios were considered. The results show that the optimized evacuation time for Bowen Island varies between 1 and 8 h, as it strongly depends on the disaster scenario, the evacuation fleet, and can be accelerated by temporary staging areas. The suitability of the approach for evacuation studies can be confirmed through the identification of key improvements for increased community resilience and the inclusion of the results in the official Bowen Island evacuation plan.
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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 | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".