Factors Influencing the Oblique Impact Test of Motorcycle Helmets
Bibliographic record
Abstract
PURPOSE: Oblique impact tests can provide important information regarding the level of protection of a helmet. Two factors that influence the results of oblique impact tests on motorcycle helmets are discussed in this work. The first factor is the angle of the anvil on which the helmet impacts. The second one is the friction between the headform and the helmet's interior. METHODS: To study the first factor, 2 anvil angles are provided, one 30° and the other one 15° to the vertical. To analyze the second factor, we consider 2 types of headform surfaces: the original metal surface of the standard headform and the same headform covered uniformly with a layer of silicone rubber that is 1 mm thick. RESULTS: The results show that varying the anvil's angle and surface friction can directly affect the linear and rotational acceleration of the headform. CONCLUSION: Testing helmets for different oblique impact angles can help assess their protection capability. The coefficient of friction between the helmet's interior and the headform plays an important role in the headform's rotational acceleration during an impact. Using a standard surface friction for headform similar or close to that of the human scalp can ensure that the results of the oblique impact tests are more consistent and realistic.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".