Donning times of marine abandonment immersion suits under simulated evacuation conditions
Bibliographic record
Abstract
Maritime emergencies often occur rapidly in unpredictable circumstances. In a scenario where a vessel or offshore installation evacuation is necessary, personal flotation and thermal protection greatly increases the chances of survival for individuals immersed in water. Marine abandonment immersion suits, intended to be donned quickly, can provide effective protection against these dangers and prolong life. The ability to locate and correctly don an immersion suit before vessel or installation abandonment is critical to survival. The Canadian immersion suit standard (CAN/CGSB-65.16-2005) dictates that a suit must be unpacked and properly donned without assistance within 2-minutes. -- Thirty-two participants, with similar knowledge and training performed donning exercises using two differing manufactures marine abandonment immersion suits under simulated maritime conditions, involving varying combinations of environmental motion and lighting states. Participant donning times, donning task errors and peak heart rates were observed for each trial. Across all conditions the mean donning time was 102.7 seconds (SD=39.6 sec), with a significant difference between donning time and suit manufacturer (p<.0001). Although overall mean donning time was below the 2-minute requirement, in total there was a 26.1% failure rate in the completion of full donning tasks within 2-minutes, with donning task error rates observed as high as 56.3%. These data suggest that the current standard should be revisited with the implementation of a more performance based, real-world applicable approach to immersion suit donning.
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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".