Civilian Helicopter Accidents into Water: Analysis of 46 Cases, 1979-2006
Bibliographic record
Abstract
BACKGROUND: When a helicopter crashes or ditches into water the crew and passengers must often make an escape from underwater and a number of the occupants do not survive. This paper examined fatality rates, human factors problems with escape, and causes of death in Canadian civilian registered helicopter accidents in water (1979-2006). METHOD: Data obtained from the Transportation Safety Board of Canada was reviewed. Key issues such as fatalities, injuries, warning time, sinking, and inversion were examined. RESULTS: There were 46 helicopters that ditched into water. There were 124 crew and passengers involved. Of those, 27 (23%) crew and passengers died. Lack of warning time (55%), rapid sinking (72%), and inversion (35%) were the most common issues in the accidents. CONCLUSION: Survival rates for Canadian registered helicopter accidents into water (78%) show little change from previously reported worldwide data. Lack of warning time, rapid sinking, and inversion were the significant factors in the survival rate. The practical implication is that crew and passengers involved in planned flights over water must wear all the life support equipment on strap-in and not have it stowed on the back of the seat or in the cabin.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".