Hierarchical structure of marine shell biomaterials: biomechanical functionalization of calcite by brachiopods
Bibliographic record
Abstract
Abstract Biologic structural materials for skeletons or teeth show a hierarchical architecture, where organic macromolecules and mineral substance form a hybrid composite material with its components inter-weaved on many length scales. On the nanostructure level brachiopods form hybrid composite mesocrystals of calcite with occluded organic molecules. On the microstructure level several types of materials are produced, on which the electron back-scatter diffraction (EBSD) technique gives insight in texture and architecture. We describe the calcite single-crystal fiber composite architecture of the secondary layer involving organic matrix membranes, the competitive-growth texture of the columnar layer and the nano-structuring of the primary layer. In the overall skeleton the organic biopolymers provide flexibility and tensile strength while the mineral provides a high elastic modulus, compressive strength, hardness and resistance to abrasion. The hierarchical composite architecture, from the nanostructure to the macroscopic level provides fracture toughness. The morphogenesis of the biomaterial as a whole and of the mineral crystals is guided by the organic matrix and most probably involves amorphous calcium carbonate (ACC) precursors. In this paper we review the hierarchical architecture of rhynchonelliform brachiopod shells, which is very distinct from mollusk nacre.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".