A comparison of the capacity of ice hockey goaltender masks for the protection from puck impacts
Bibliographic record
Abstract
Goaltenders in ice hockey are the only players that are on the ice for the entire game. Their position exposes them to impacts from collisions with other players, falls to the ice, and puck impacts. In competitive ice hockey leagues, head injuries resulting from puck impacts have been reported with some cases resulting in ending the player's career. Considerable research has been conducted to assess the performance of hockey helmets; however, few have assessed the performance of goaltenders' masks. The purpose of this study was to compare the capacity of four goaltenders' masks for the protection from puck impact as measured by head acceleration and peak force. A Hybrid III headform was fitted with four different goaltender masks and impacted with a hockey puck in three locations at 25 m/s. The masks were found to vary in the level of protection they offered as the mask with the thickest liner resulted in lower forces than the thinnest mask for side impacts; however, the thinnest mask resulted in the lowest force for front impacts. Despite performance differences at specific locations, no one mask proved to be superior as peak acceleration and peak force values did not exceed the thresholds necessary for concussion.
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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 | 0.002 |
| 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".