Historical Maritime Search and Rescue Incident Data Analysis
Bibliographic record
Abstract
Since the 1980s the Canadian Coast Guard has maintained a database of maritime search and rescue (SAR) incidents involving response assets and personnel. This information is stored in a national database known as the Search and Rescue Program Information Management System (SISAR). SISAR contains a spatiotemporal record for all serious incidents that occur within Canada’s coastal search and rescue area. In addition to providing a record of all response operations, it provides a rich historical dataset for analysts to use to support a wide range of decision-making applications. In this chapter we illustrate the use of SISAR incident data to identify and visualize temporal and spatial patterns in the maritime SAR incident data. Temporal phenomena were examined at three temporal scales: yearly, monthly, and hourly. Spatial phenomena were examined using the spatial location and density of incidents. Several useful visualizations to explore and exploit SISAR data are provided. Lastly, we provide a short discussion of several topics relevant to SAR incident analysis, including (1) under-reporting in incident databases, (2) sharing of national SAR incident data, and (3) linking environmental conditions and accident data to add context to historical SAR incidents and to improve SAR response time estimation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.016 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".