Survival from Canadian Seaplane Water Accidents: 1995 to 2019
Bibliographic record
Abstract
INTRODUCTION: Each year in Canada, there are a number of pilots and passengers who die in seaplane water accidents. A study examining the human factors and fatality rates associated with these accidents was conducted. METHODS: Seaplane water accident investigations by the Transportation Safety Board of Canada (TSB) between 1995 and 2019 were reviewed. RESULTS: There were 487 accidents involving 1144 occupants (487 pilots, 657 passengers). There were less than 15 s warning in 86% of cases. There were 60 pilots and 88 passengers who dieda survival rate of 87%. Drowning, trapped within the cabin was the principal cause of death (54%). Loss of control on landing, wheels down landings, and other landing problems (49%) were the principal causes of the accidents and 77% of the fatalities occurred in this group. These arose because the pilot(s) misjudged wind, waves, and glassy water. Over 50% of seaplanes inverted and 10% floated briefly then sank, resulting in the highest percentage of fatalities. Wearing the seat harness incorrectly, injury, in-rushing water, and inability to locate and operate exit mechanisms (including rescuers inability to open the exits external to the fuselage) all contributed to the fatalities. Life jackets would have been of benefit in several cases. Of the accidents, 57% were private flights. CONCLUSIONS: Passengers require a thorough preflight briefing, life jackets should be worn by all pilots and passengers, and private and commercial pilots should receive Underwater Egress Training. MacDonald C, Brooks C, McGowan R. Survival from Canadian seaplane water accidents: 1995 to 2019. Aerosp Med Hum Perform. 2021; 92(10):798-805.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".