High Strain Rate Compressive Properties of Soft Tissue
Bibliographic record
Abstract
High strain rate material properties and constitutive equations are essential for the development of numerical and physical models to assess the performance of soft materials subject to high rate deformation, with potential applications including protective equipment and vehicle crashworthiness. However, these properties are not available for many soft tissues. This is because specialized testing methods must be employed to obtain the necessary data. Fresh bovine tissue from the semimembranosis muscle was obtained and tested using a polymeric Split Hopkinson Pressure Bar. Samples were tested from 1.4 to 200 hours post mortem to observe the effect of rigor and other possible temporal effects on the material properties. Since this muscle had relatively uniform fiber orientation, it was possible to obtain specimens with fiber directions parallel, perpendicular, and at 45 degrees to the compression axis. The stress-strain curves for the muscle were concave upwards, as is typical of soft tissues at high strain rates. Fiber orientation was determined to have negligible effect at the tested strain rates. The testing revealed that the stiffness of the tissue increased with post mortem time until approximately 6 hours. At times greater than 200 hours post mortem, the tissue properties were found to be very similar to the properties of fresh tissue. These findings suggest that properties of fresh tissue might be estimated using more easily obtained post-rigor tissue.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".