Collagen network primarily controls Poisson's ratio of bovine articular cartilage in compression
Bibliographic record
Abstract
The equilibrium Young's modulus of articular cartilage is known to be primarily determined by proteoglycans (PGs). However, the relation between the Poisson's ratio and the composition and structure of articular cartilage is more unclear. In this study, we determined Young's modulus and Poisson's ratio of bovine articular cartilage in unconfined compression. Subsequently, the same samples, taken from bovine knee (femoral, patellar and tibial cartilage) and shoulder (humeral cartilage) joints, were processed for quantitative microscopic analysis of PGs, collagen content, and collagen architecture. The Young's modulus, Poisson's ratio, PG content (estimated with optical density measurements), collagen content, and birefringence showed significant topographical variation (p < 0.05) among the test sites. Experimentally the Young's modulus was strongly determined by the tissue PG content (r = 0.86, p < 0.05). Poisson's ratio revealed a significant negative linear correlation (r = -0.59, p < 0.05) with the collagen content, as assessed by the Fourier transform infrared imaging. Finite element analyses, conducted using a fibril reinforced biphasic model, indicated that the mechanical properties of the collagen network strongly affected the Poisson's ratio. We conclude that Poisson's ratio of articular cartilage is primarily controlled by the content and organization of the collagen network.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".