Historical Maritime Search and Rescue Incident Data Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it