The Mechanical Characterization and Comparison of Male and Female Calvaria Under Four-Point Bending Impacts
Bibliographic record
Abstract
The circumstances in which we mechanically test and critically assess human calvarium tissue would find relevance under conditions encompassing real-world head impacts. These conditions include, among other variables, impact velocities, and strain rates. Compared to quasi-static loading on calvaria, there is less reporting on the impact loading of the calvaria and consequently, there are relatively fewer mechanical properties on calvaria at relevant impact loading rates available in the literature. The purpose of this work was to report on the mechanical response of 23 human calvarium specimens subjected to dynamic four-point bending impacts. Impacts were performed using a custom-built four-point impact apparatus at impact velocities of 0.86-0.89 m/s resulting in surface strain rates of 2-3/s-representative of strain rates observed in vehicle collisions and blunt impacts. The study revealed comparable effective bending moduli (11-15 GPa) to the limited work reported on the impact mechanics of calvaria in the literature, however, fracture bending stress (10-47 MPa) was relatively less. As expected, surface strains at fracture (0.21-0.25%) were less compared to studies that performed quasi-static bending. Moreover, the study revealed no significant differences in mechanical response between male and female calvaria. The findings presented in this work are relevant to many areas including validating surrogate skull fracture models in silico or laboratory during impact and optimizing protective devices used by civilians to reduce the risk of a serious head injury.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".