An Evaluation of Consequence Severity of Ship Evacuations in the Canadian Arctic
Bibliographic record
Abstract
Abstract Ship evacuations in Arctic waters expose crew and passengers to the potential for severe life-safety consequences. Effective planning and allocation of Arctic SAR services should be supported by evidence-based assessments of the consequence severity of ship evacuations. This paper provides an evaluation of life-safety consequence severity for ship evacuation scenarios in the Canadian Arctic. Exposure time is a predominant factor influencing consequence severity of Arctic ship evacuations. The study integrates existing models for exposure time estimation [1,2] and life-safety consequence [3]. Exposure time is estimated for air- and marine-based SAR assets, considering associated capacities, speeds, and operating ranges. Evacuation scenario factors include ship type, number of POB, and geographic location. The methodology provides Arctic SAR service providers with a tool for planning effective resource allocation and response efforts. As a base case, exposure times and consequence severities are estimated for SAR response with a single asset. The effect of deploying multiple SAR assets is demonstrated. Results indicate that the deployment of air-based SAR assets contributes to reduced exposure times and mitigation of life-safety consequence severity. Evacuation of a passenger vessel is a worst-case scenario. Evacuations of high POB vessels require the deployment of multiple SAR assets to prevent potential disastrous life-safety consequences.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".