Bibliographic record
Abstract
Technologies, engineering and analysis, and regulatory provisions relating to arctic Escape Evacuation and Rescue (EER) today and in development are reviewed. Current national and international regulatory performance based regime has necessitated the development of tools for the evaluation and setting of performance based goals such as availability and reliability requirements, mirrored in current Transport Canada and ISO standards. To facilitate setting of reliability targets in the Canadian standards, a multifaceted research and development program was initiated in parallel with its regulatory developments. Full scale manned and model tests, engineering and computer simulation, and world wide consultations and studies on human performance in life threatening conditions, comprise this R&D programme. Concurrently, industry and the private sector have addressed the frontier and arctic EER needs through analyses, development of novel systems, and participation in framing performance based standards. Use of conventional EER systems and technologies was found to have limited applicability in ice populated waters, requiring the development of systems and procedures suited to the environmental, operational, and logistical requirements of arctic offshore regions. The paper summarizes available and emerging regulatory, modeling and research, and technological developments in arctic EER and gives recommendations on a number of promising directions.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.013 |
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".