Bibliographic record
Abstract
"The contents of this book touch on the all major dental biomaterials: polymers, composites, ceramics and metals. The first part introduces the readers to the surface physicochemical and mechanical characterizations at the nanoscopic level, and the use of finite element analysis. The second part discusses dental adhesion, resin-based composites, polymerization contraction stress, impression materials and soft liners for total prosthesis. The third part deals with ceramics in restorative dentistry: zirconia and lithium disilicate, the fractography of dental ceramics, as well as bioglass for bone growth. The fourth part discusses the toxicity of mercury in dentistry, and the use of preventive materials for dental diseases. The concluding part identifies imminent techniques for dental biomaterials, such as additive manufacturing (3D printing), and bioprinting in dentistry."--
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.000 |
| Insufficient payload (model declined to judge) | 0.083 | 0.026 |
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; both teacher heads agree on what is shown here.
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".