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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 teacher head, 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".