Clothing Buoyancy and Underwater Horizontal Swim Distance After Exiting a Submersed Vehicle Simulator
Bibliographic record
Abstract
BACKGROUND: Winter road workers, who drive heavy vehicles over ice-covered waterways, are at risk for ice failure, vehicle submersion, and subsequent drowning in frigid water. Although some jurisdictions require these workers to wear flotation clothing, there are concerns that, following an underwater exit in fast-moving water, increased clothing buoyancy may reduce ability to swim against the current to safely return to the ice opening. METHODS: Using a simulator in a swimming pool (3.7 m deep, 28 degrees C), 11 volunteers (5 women) were submersed 8 times each to test the effects from both an Upright and an Inverted position of a normal nonflotation winter jacket (Control), a flotation Jacket, a flotation Overall, and a personal inflatable vest which was inflated (Inflated Vest) on underwater horizontal swim distance. Subjects also rated exit difficulty and impedance, psychological stress, and thermal comfort. RESULTS: Compared to Control, Jacket, and Overall, the Inflated Vest generally increased exit difficulty, escape impedance, and psychological stress, while greatly decreasing the ability to swim horizontally underwater before reaching the surface (Control, 6.1 m; Jacket, 5.0 m; Overall, 3.4 m; and Inflated Vest only 1.4 m). Swim distance with the Overall was also significantly shorter than Control, but not Jacket. DISCUSSION: Flotation clothing (either Jackets or Overalls) is recommended for vehicle travel on ice because they do not impede underwater exit from a vehicle and allow significant horizontal underwater swim distance. An inflatable vest is not recommended because inappropriate premature inflation could increase exit impedance and decreased underwater swim distance.
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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) | 0.001 | 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".