Escape from a Submersible Vehicle Simulator Wearing Different Thermoprotective Flotation Clothing
Bibliographic record
Abstract
BACKGROUND: Winter road workers, who drive heavy vehicles on ice-covered waterways, are at risk for ice failure and subsequent drowning in frigid water. Some workers who are recommended to wear thermoprotective flotation clothing are concerned that buoyancy or bulk may impede underwater exit. METHODS: Using a simulator, 10 volunteers (2 women) compared everyday winter clothing (Control), a flotation Jacket and Overall, and an inflated inflatable personal flotation device (Inflated Vest). On each study day, all clothing conditions were tested in either Cool (20 degrees C) or Cold (8 degrees C) water conditions using a randomized balance design. After each trial, subjective ratings for thermal sensation and exit tasks along with exit task times were determined. RESULTS: Exit task times were unaffected by clothing or water conditions. Compared to Control, the Inflated Vest was rated with higher exit task difficulty and impedance, while the Jacket and Overall were not (ratings for exit task difficulty and impedance in cold water were: Control, 'a little' and 'none'; Jacket, 'a little' and 'a little'; Overall, 'a little' and 'moderate'; and Inflated Vest, 'moderate' and 'moderate - a lot'). Finally, there was a training effect, with total exit times improving by 20% from trials 1-8 (12.3 to 9.8 s). CONCLUSIONS: Results suggest that, compared to Control clothing, flotation Jackets and Overalls do not increase exit time or impede exit during egress from a submerged vehicle while providing thermoprotection and buoyancy in 20 degrees C and 8 degrees C water. The Inflated Vest created the most perceived exit impedance in comparison to Control.
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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 | 0.000 | 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) | 0.002 | 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".