The basis for the development of a fuselage evacuation time for a ditched helicopter.
Bibliographic record
Abstract
HYPOTHESIS: When a helicopter ditches or crashes in water, unless the buoyancy bags are inflated, it commonly sinks inverted. Thus, crew and passengers must make an underwater escape. It is postulated that later passengers in the escape sequence do not have the breath-holding ability to conduct a successful escape, particularly if the water is cold. This contributes to the 20-50% mortality rate in survivable accidents. METHODS: There were 132 immersed subject evaluations which were conducted in daylight and darkness to measure escape times from a helicopter underwater escape trainer, configured to the Super Puma, seated for 15 and 18 passengers. The subjects were highly experienced instructors or Navy clearance divers. RESULTS: The time from when each subject's head disappeared underwater until each subject surfaced and total fuselage evacuation time were measured and any problems hampering escape were noted. Breath-holding for the last subject out ranged from 28 to 92 s. An emergency breathing system was used by a minimum of four subjects each time and a maximum of 11 subjects in one condition. The buoyancy of the survival suit was the principal component that hampered escape. CONCLUSION: Breath-holding times were too long for the later subjects to escape without resorting to an EBS, in spite of the fact that they were highly trained. For regular crew and passengers flying over water, this would explain the high mortality, etc. Therefore, a new helicopter standard should be developed requiring fuselage design to accommodate total evacuation within 20 s from underwater. For current helicopters, where this cannot be achieved, passengers should be provided with some form of air supply, or, after ditching, the helicopter should be modified so that it will stay afloat on its side and retain an air space 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".