Stopping Distance and Acceleration Performance of Personal Watercraft
Bibliographic record
Abstract
This study investigates the stopping distance and acceleration performance of five commercially available personal watercraft (PWC). Testing was performed to develop data for the reconstructionist performing analyses of PWC accidents. Stopping distances were determined by integrating velocity measurements collected by a Ka band radar and data acquisition system. Acceleration measurements were determined by differentiating data collected using the same equipment. Typical stopping distances were found to be 125 ft to 160 ft (38.1 m to 48.8 m) at 30 mph (48 km/h) and 180 ft to 225 ft (54.9 m to 68.6 m) at 40 mph (64 km/h). For the range of velocities tested, stopping distance was found to be linearly related to speed. The average deceleration over the full stopping distance was -0.14 g to -0.31 g. If these stopping distances and accelerations are compared to road vehicles, they are similar to those observed on snow and ice. Straight line acceleration was observed to be speed dependent. Each particular watercraft had its own acceleration versus speed profile. Average peak accelerations observed were 0.31 g to 0.55 g. For the most powerful watercraft, the average acceleration from 0 to 30 mph (48 km/h) was over 0.45 g. This is similar to the average acceleration of a 2004 Porsche Boxster accelerating from 0 - 62.0 mph (0 - 100 km/h) in 6.4 seconds for an average acceleration of 0.44 g.
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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 | 0.001 | 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 | 0.001 |
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".