Determination of high-risk impact sites on a Hybrid III headform by finite element analysis
Bibliographic record
Abstract
The current standards and methods to evaluate helmet performance remain focused on traumatic brain injuries and not concussive injuries. This is reflected in the methodologies currently used and the injury metrics employed to determine the pass/fail criteria for the helmets being tested. To address the problem surrounding concussion and helmets, a method reflecting high risk of concussive injury must be developed. The purpose of this research was to identify high risk of concussive injury impact sites on the Hybrid III headform using the Wayne State head injury finite element model. The Hybrid III headform was impacted using a linear impactor in five different sites with four angles per site at 5.5 m/s. The resulting acceleration loading curves were used as input for brain deformation analyses using the Wayne State University Brain Injury Model. The brain deformation results indicated that there are 12 impact conditions on the Hybrid III headform which reflect a risk of concussion above 80% when compared with the literature. The method used and impact sites discovered by this research differ significantly from the current standard method and may be used to guide future impact sites for the evaluation of helmet performance.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".