The Ultrastructure of Bone and Its Relevance to Mechanical Properties
Bibliographic record
Abstract
Bone is a biologically generated composite material comprised of two major structural components: crystals of apatite and collagen fibrils. Computational analysis of the mechanical properties of bone must make assumptions about the geometric and topological relationships between these components. Recent transmission electron microscope (TEM) studies of samples of bone prepared using ion milling methods have revealed important previously unrecognized features in the ultrastructure of bone. These studies show that most of the mineral in bone lies outside the fibrils and is organized into elongated plates 5 nanometers (nm) thick, ~ 80 nm wide and hundreds of nm long. These so-called mineral lamellae (MLs) are mosaics of single 5 nm-thick, 20 – 50 nm wide crystals bonded at their edges. MLs occur either stacked around the 50 nm-diameter collagen fibrils, or in parallel stacks of 5 or more MLs situated between fibrils. About 20% of mineral is in gap zones within the fibrils. MLs are apparently glued together into mechanically coherent stacks which break across the stack rather than delaminating. ML stacks should behave as cohesive units during bone deformation. Finite element computations of mechanical properties of bone show that the model including such features generates greater stiffness and strength than are obtained using conventional models in which most of the mineral, in the form of isolated crystals, is situated inside collagen fibrils.
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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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".