Off-Throttle Turning Performance of Personal Watercraft for Accident Reconstruction
Bibliographic record
Abstract
Lack of operator control during off-throttle steering probably has contributed to many personal watercraft (PWC) collisions. Most PWCs are not equipped with any type of off-throttle steering devices. In this study, the average off-throttle steering trajectories were investigated for four commercially available PWCs at a test speed of 30 mph. An add-on device was tested on one of the PWCs to determine whether off-throttle turning performance could be improved. The off-throttle performance of a 16 ft runabout with a 140 hp outboard motor was also tested for comparison. The watercraft were tested according to the SAE J2608 Recommended Practice, Off-Throttle Steering Capabilities of Personal Watercraft. The trajectories of these watercraft were quantified in an off-throttle steering maneuver. These trajectories can be used as a guideline to reconstruct PWC accidents. The off-throttle turning capabilities of the test watercraft can be classified into two categories: those with effective off-throttle turning capability and those without effective off-throttle turning capability. Results showed that PWCs not equipped with an off-throttle steering device had virtually no turning capabilities. PWCs with off-throttle steering devices demonstrated substantially enhanced turning capabilities. The runabout also demonstrated superior off-throttle steering performance.
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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".