Current Helicopter Underwater Egress Knowledge: Are we Prepared for the Next Ditching?
Bibliographic record
Abstract
Abstract The purpose of this paper is to highlight the current helicopter underwater egress knowledge available from various online sources. Publicly available online sources were used as a way of ensuring that anyone with Internet access would be able use the information as opposed to a private library of technical documents or exclusive access to particular journal databases. Given that more than 30 years of underwater egress training has been completed, it was expected that the majority of the papers/reports available would be directed at training methodologies, ditching statistics, and factors affecting survival. A total of 18,862 entries were identified from six different search engines. Of the nearly 19,000 items, only 112 (1%) were considered relevant. From the directed search, it appears that a considerable amount of work has been carried out to identify the factors affecting egress as well as in the area of accident investigation. Surprisingly however, only 14% of the 112 selected documents address aspects of underwater egress training and just 2% directly address the retention of skills. A total of 10 (9%) consider ditching statistics, and 49 (44%) address the factors affecting egress. Based on the findings from this focused examination of available underwater egress literature, it is clear that further work needs to be directed toward how to best prepare the global offshore workforce for a ditching/water impact.
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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".