From body‐on‐frame to unibody constructions and designs mimicking biological structures – an overview
Bibliographic record
Abstract
In dentistry, isotropic materials, such as metals, ceramics, and polymers, are used. Their properties are not related to any specific direction of the material microstructure. There is a trend toward non-metallic, adhesive, and minimally invasive dentistry. This is in line with the conceptual change seen in the automobile industry, in which the basis of car structures has changed from body-on-frame designs to unibody designs. In unibody designs, all structural parts of the body of the car mechanically form a single structural entity. In modern adhesive dentistry, remaining tooth substance and the dental material form unibody designs, enabling preservation of tooth substance. Biological structures are created to withstand loading and are light in weight. The structural designs of elements in these biological materials are, to a large extent, based on fibrous material. More attention has been paid to mimicking fibrous structures of dental hard tissues by synthetic fiber-reinforced composites. This overview reports key features of natural fibrous elements and how they are utilized in dentistry. Special emphasis is placed on the aspects of interfacial adhesion of restorative materials, especially ceramics to resin-based materials and their role in the unibody design of the tooth-restoration system.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".