Development of a novel test surrogate for evaluating material protection against hockey puck impacts to the neck and clavicle
Bibliographic record
Abstract
During ice hockey, pucks travel at high speeds and have the potential to cause substantial injury. The neck and torso are vulnerable locations where protective materials could mitigate impacts and reduce injury. Currently, neck guards are tested solely for cut protection, while no known standards exist for chest protection. To assess products and materials, a novel test surrogate was required with realistic body geometry (for product mounting) and stiffness (reflecting body compliance). The neck and clavicle were instrumented, and hockey puck impacts were applied to each in an unpadded condition, as well as with eight foams and three commercial neck guards in place. Protection of each material was quantified as the reduction in force relative to the unpadded condition. The greatest force reduction with a foam was 42%–46% (neck-clavicle), supporting its potential use in equipment. Only one of the commercial neck guards provided impact resistance (39% force reduction, vs 4%–6% in the others). Without injury tolerance data for the neck, it is unknown if the materials would have prevented injury; none reduced load below the reported fracture tolerance of the clavicle. This test surrogate and investigation may inform the future development of standards for improved hockey equipment design and evaluation.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".