Helicopter Crashes into Water: Warning Time, Final Position, and Other Factors Affecting Survival
Bibliographic record
Abstract
INTRODUCTION: According to 40 yr of data, the fatality rate for a helicopter crash into water is approximately 25%. Does warning time and the final position of the helicopter in the water influence the survival rate? METHODS: The National Transportation Safety Board (NTSB) database was queried to identify helicopter crashes into water between 1981 and 2011 in the Gulf of Mexico and Hawaii. Fatality rate, amount of warning time prior to the crash, and final position of the helicopter were identified. RESULTS: There were 133 helicopters that crashed into water with 456 crew and passengers. Of these, 119 occupants (26%) did not survive; of those who did survive, 38% were injured. Twelve died after making a successful escape from the helicopter. Crashes with < 15 s warning had a fatality rate of 22%, compared to 12% for 16-60 s warning and 5% for > 1 min. However, more than half of fatalities (57%) came from crashes for which the warning time could not be determined. DISCUSSION: Lack of warning time and how to survive in the water after the crash should be a topic for study in all marine survival/aircraft ditching courses. Investigators should be trained to provide estimates of warning time when investigating helicopter crashes into water.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".