Search and rescue (SAR) modeling for the coastal regions of Eastern Canada and the Arctic Gateway
Bibliographic record
Abstract
The Search and Rescue (SAR) system plays a critical role in ensuring the safety of maritime activities in Eastern Canada and the Arctic Gateway. This thesis presents a comprehensive method for assessing SAR time in the region, specifically focusing on scenarios where helicopters are utilized as the primary rescue resource. The developed macro-scale SAR model incorporates a Discrete Event simulation approach with stochastic elements to account for uncertainties and variability inherent in SAR operations. By utilizing the SAR model, a wide range of scenarios can be analyzed, allowing users to define various factors such as helicopter deployment time variability, helicopter parameters, and more. The model employs a time-stepping approach, enabling real-time decision-making and operational adjustments at each time step. It considers multiple factors, including incident and helicopter location, weather conditions, and the number of individuals in distress, to assess SAR effectiveness. The model underwent rigorous verification tests, demonstrating close alignment with hand calculation methods. Furthermore, a validation test was conducted using data from a real-life incident involving the Viking Sky, where the model's predictions closely matched the actual incident timeline within a certain percentage of accuracy. The model was further utilized to examine the influence of incident location, the number of survivors, and refueling requirements systematically. Additionally, Arctic-based scenarios were explored to account for specific conditions in the Arctic region. The research findings indicate that incident location, the number of individuals in distress, and weather conditions significantly impact SAR time. Specifically, the total rescue time shows a greater increase with distance from the helicopter base compared to the number of survivors, particularly for smaller survivor groups. When the helicopter base was relocated to an Arctic location, the total rescue time for smaller survivor groups was halved. The importance of optimizing the location of SAR assets and facilities is emphasized throughout the research. The study also examines the effects of operating two or more helicopters simultaneously on SAR time, providing insights into its impact. Overall, this thesis underscores the importance of continuous improvement and collaboration to enhance SAR capabilities and ensure maritime safety in the coastal regions of Eastern Canada and the Arctic Gateway. The findings contribute valuable insights for policymakers, SAR organizations, and stakeholders involved in the maritime domain, aiming to reduce response times, increase operational efficiency, and ultimately save lives at sea.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".