Snowmobile Pole Crash Tests
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">Instrumented crash tests are a valuable source of information for collision reconstruction, as the collected data allows for a better understanding of the dynamics and severity of real-world collisions. Numerous published crash tests exist for automobiles, motorcycles, and heavy vehicles; however, none of the published crash testing has involved snowmobiles. This paper presents the results of six snowmobile crash tests to begin to fill the gap in the literature. In five tests, the test snowmobile was accelerated forward into a pole, made from a tree trunk 33 cm in diameter. In the last test, two snowmobiles collided head-on into each other.</div><div class="htmlview paragraph">Prior to testing, each snowmobile was weighed and scanned using a Faro 3D scanner. All of the snowmobiles were instrumented to collect speed and acceleration data during the tests. Each snowmobile was scanned after the test, which allowed for measurements of the extent of crush from impact.</div></div>
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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 | 0.001 |
| 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".