Determining Effective Centroid Position in Biomechanical Testing: A Technique for Simplifying Whole Bone Analysis
Bibliographic record
Abstract
BACKGROUND: Whole bone in vitro biomechanical compressive testing can be complicated by three factors: sample asymmetry, heterogeneous material properties, and unknown effective centroid location. METHOD OF APPROACH: The technique presented here facilitates the calculation of effective centroid position, modulus of elasticity and equivalent uniform strain magnitude for a cross section of bone from a simple whole bone compressive test. Simplification of section response to load is achieved through a combination of linear beam and simple planer geometry theory. The technique requires three longitudinal strain gauges be affixed around the test specimen cross section of interest, gauge position need not be determined. Sample loading is then accomplished using a simple four point loading jig. RESULTS: Results of the technique are presented on an object with known elasticity and geometry, an aluminium tube, and seven pairs of equine third metacarpal whole bones. CONCLUSIONS: Average cross section modulus of elasticity, equivalent uniform cross section strain, and effective centroid locations were all predicted to within the range of published values. Employing the testing setup and analysis technique presented in this paper resulted in a significant savings in both implementation complexity and cost over previously available techniques.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".