My Car Is Sinking: Automobile Submersion, Lessons in Vehicle Escape
Bibliographic record
Abstract
INTRODUCTION: In North America approximately 400 individuals per year die in submersed vehicles, accounting for 5-11% of all drownings. About half of people surveyed would let the vehicle fill with water before attempting exit. METHODS: We used a crane and two passenger vehicles of the same make, model, and year-one with passenger compartment intact (I) and one with holes (H) in the floor (area approximately 2200 cm2)--to conduct occupied and unoccupied submersions. RESULTS: Three phases of submersion were identified: 1) FLOATING, vehicles floated for 15 s (H) to 63 s (I) before the water reached the bottom of the side windows; 2) SINKING, the subsequent period until the vehicle is completely under water, but before it fills completely; and 3) SUBMERGED, the vehicle was full of water and several feet below the surface. Total time to submersion was 150 s for I but only 37 s for H. Opening the door to exit Vehicle I decreased submersion time from 150 to 30 s. Even the most difficult exit strategy attempted (three men and a child manikin through one window) was quickly performed from Vehicle I (only 51 s). During one exit attempt, initiated during the sinking phase, it was impossible to open the doors or windows until the vehicle was completely full of water. CONCLUSIONS: A vehicle is most easily exited during the initial Floating Phase. We suggest the following escape procedure: SEATBELT(s) unfastened; WINDOWS open; CHILDREN released from restraints and brought close to an adult; and OUT, children should exit first.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".