Impact Performance Characteristics of Hockey Helmets with Liners of Differing Thicknesses
Bibliographic record
Abstract
Recently professional players have been modifying their hockey helmets by using liner materials which have a thickness of only 9.5 mm compared to the usual 16 mm thickness of liner found in many Canadian Standards Association (CS A) approved helmets. Since helmet performance is related to both the stiffness and thickness of the liner material, this study was conducted to test the hypothesis that the performance capability of helmets with thinner liners is substantially less than that of CSA approved helmets. Five helmet models with vinyl nitril liners of the same density but of two differing thicknesses (9.5 mm and 16 mm) were subjected to 3 successive impacts each on the side location using a standard monorail drop test apparatus. The headform was a rubberized epoxy alloy from which the headform acceleration and Severity Index were determined. The average peak acceleration for the third impact for the regular liner (RL) was 261±13g compared to 367±28g for the thin liner (TL). The average Severity Indices associated with these impacts were 1025±73 for the RL and 1615±175 for the TL. Using the graph of Prasad Mertz (1985) a Severity Index (SI) of 1000 would put about 12% of the population at risk for a serious head injury while an SI of 1600 would put the risk at about 48% of the population. Clearly, when helmet liners of the same density but with different thicknesses are used, the risk of injury is magnified with the thin liner. To improve head protection with a thin liner additional research using stiffer materials is required.
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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) | 0.001 | 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 | 0.001 |
| 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".