Disorientation in Helicopter Ditching and Rigid Inflatable Boat Capsizement: Training is Essential to Save Crews
Bibliographic record
Abstract
This paper discusses the disorientation problems of escape from a rigid inflatable boat (RIB) that has been capsized. It makes comparisons with executing a ditched helicopter underwater escape and emphasizes the need for realistic training for both RIB and helicopter crafts. Although very poor records are collected on RIB capsizements, each year there is a small but significant loss of life and many close calls. A paper at the Royal Institute of Naval Architects in 1998, reported 13 deaths from an accident involving the Sea Gem in 1965, but gave no further details (Reference 5). The Transportation Safety Board of Canada reported the case of the G.R. 1 FRC (Reference 3) launched from the Gordon Reid off British Columbia, which grounded and flung the three occupants over the rocks and back into the water. Miraculously, all three survived. Rigid inflatable boats or fast rescue crafts (FRC) are used by every Navy in the world, as well as many other paramilitary and commercial marine organizations. In 1998, it was reported that the US Coast Guard alone operated over 700 FRCs (Reference 5). To date, no one has examined the problem of escape from such a vessel after it has been capsized, although Oakley has examined the pros and cons of wearing head protection while operating small, fast boats (Reference 2). This paper discusses a recent experiment conducted by Survival Systems to examine the problems of underwater escape from a capsized FRC.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".