Introduction of a Compressed Air Breathing Apparatus for the Offshore Oil and Gas Industry
Bibliographic record
Abstract
INTRODUCTION: When a helicopter ditches the majority of crew and passengers have to make an underwater escape. Some may not be able to hold their breath and will drown. For at least 15 yr, military aircrew have been trained to use a scuba system. In the offshore oil and gas industry, there has been more caution about introducing a compressed air system and a rebreather system has been introduced as an alternative. Recently, Canadian industry and authorities approved the introduction of Helicopter Underwater Emergency Breathing Apparatus (HUEBA) training using compressed air. This communication reports the training of the first 1000 personnel. METHODS: Training was introduced in both Nova Scotia and Newfoundland concurrently by the same group of instructors. Trainees filled out a questionnaire concerning their perceived ratings of the ease or difficulty of classroom training and the practical use of the HUEBA. RESULTS: Ninety-eight percent of trainees found the classroom and in-water training to be "good/very good". Trainees found it to be "easy/very easy" to clear the HUEBA and breathe underwater in 84% and 64% of cases, respectively. Divers reported a greater ease in learning all the practical uses of the HUEBA except application of the nose clip. DISCUSSION: There were problems with the nose clip fitting incorrectly, and interference of the survival suit hood with the regulator, which subsequently have been resolved. When carefully applied, the introduction of the HUEBA into training for offshore oil and gas industry helicopter crew and passengers can be safely conducted.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".