Bimodal collagen fibril diameter distributions direct age-related variations in tendon resilience and resistance to rupture
Bibliographic record
Abstract
Scaling relationships have been formulated to investigate the influence of collagen fibril diameter (D) on age-related variations in the strain energy density of tendon. Transmission electron microscopy was used to quantify D in tail tendon from 1.7- to 35.3-mo-old (C57BL/6) male mice. Frequency histograms of D for all age groups were modeled as two normally distributed subpopulations with smaller (D(D1)) and larger (D(D2)) mean Ds, respectively. Both D(D1) and D(D2) increase from 1.6 to 4.0 mo but decrease thereafter. From tensile tests to rupture, two strain energy densities were calculated: 1) u(E) [from initial loading until the yield stress (σ(Y))], which contributes primarily to tendon resilience, and 2) u(F) [from σ(Y) through the maximum stress (σ(U)) until rupture], which relates primarily to resistance of the tendons to rupture. As measured by the normalized strain energy densities u(E)/σ(Y) and u(F)/σ(U), both the resilience and resistance to rupture increase with increasing age and peak at 23.0 and 4.0 mo, respectively, before decreasing thereafter. Multiple regression analysis reveals that increases in u(E)/σ(Y) (resilience energy) are associated with decreases in D(D1) and increases in D(D2), whereas u(F)/σ(U) (rupture energy) is associated with increases in D(D1) alone. These findings support a model where age-related variations in tendon resilience and resistance to rupture can be directed by subtle changes in the bimodal distribution of Ds.
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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 | 0.001 | 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 | 0.000 | 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".