Comparison between Hybrid III Headforms by Linear and Angular Dynamic Impact Response Characteristics
Bibliographic record
Abstract
The development of surrogate headforms with similar dimensions and weight to that of a human head has allowed researchers to collect dynamic impact response data for impact reconstructions and injury assessment. These headforms are relied upon to deliver accurate and repeatable dynamic impact response data for setting helmet certification standards as well as head injury reconstruction. With recent research demonstrating the importance of measuring three dimensional dynamic impact response characteristics, the Hybrid III headform is a potentially a good candidate for use in standards testing and impact reconstructions. Currently, this headform is validated with a single 37.6-cm drop to the front region of the headform with an acceptance window of 50 g. Therefore, the purpose of this study was to compare the dynamic impact response of two Hybrid III headforms and verify repeatability, compare dynamic impact response, and determine how closely the two headforms correlate across different impact conditions. Two Hybrid III headforms were dropped from nine heights at two impact locations (front and side). Results of this study show that the two headforms are highly correlated across drop heights. Significant differences in terms of dynamic impact response were found between the two headforms across impact conditions. This study showed that two Hybrid III headforms produce similar mean peak linear acceleration for front centric impacts, however, differ significantly for mean peak angular response.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".