Test Data for Use in the Reconstruction of Collisions Involving Motorized Snow Vehicles
Bibliographic record
Abstract
In February 2000, collision investigators from the Ontario Provincial Police North East Region gathered at the Canadian Forces Base in North Bay, Ontario, Canada, to conduct testing sessions involving motorized snow vehicles. The testing was organized due to the relative shortage and need for accurate data for when this type of machine is involved in a collision. Motorized snow vehicles are common in a great many areas of Canada and the U.S.. The need for accurate current data was most required by investigators within the Ontario Provincial Police, primarily in northern areas of Ontario. The testing was deemed successful and in February 2002, further testing was conducted. The testing sessions verified earlier testing data reported by several authors. Areas addressed were skid to stop, roll down to a stop, airborne, rollover, acceleration, perception, and reaction. Four Bombardier Safari 377 cc short track snow machines without carbide studs were used and performed well during the sessions. Results are reported and discussed.
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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 | 0.002 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".