In tendons, differing physiological requirements lead to functionally distinct nanostructures
Bibliographic record
Abstract
The collagen-based tissues of animals are hierarchical structures: even tendon, the simplest collagenous tissue, has seven to eight levels of hierarchy. Tailoring tissue structure to match physiological function can occur at many different levels. We wanted to know if the control of tissue architecture to achieve function extends down to the nanoscale level of the individual, cable-like collagen fibrils. Using tendons from young adult bovine forelimbs, we performed stress-strain experiments on single collagen fibrils extracted from tendons with positional function, and tendons with energy storing function. Collagen fibrils from the two tendon types, which have known differences in intermolecular crosslinking, showed numerous differences in their responses to elongation. Unlike those from positional tendons, fibrils from energy storing tendons showed high strain stiffening and resistance to disruption in both molecular packing and conformation, helping to explain how these high stress tissues withstand millions of loading cycles with little reparative remodeling. Functional differences in load-bearing tissues are accompanied by important differences in nanoscale collagen fibril structure.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| 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".