Bibliographic record
Abstract
Abstract The sections in this article are Introduction Classification of Lasers in Restorative Dentistry Lasers and Delivery Systems Laser–Dental Tissue Interaction Laser Effects on Dental Tissues Laser Effect on Enamel Laser Effects on Dentin Laser Effects on Dental Pulp Lasers in Operative Dentistry Laser‐Assisted Tooth Bleaching Laser‐Assisted Cavity Preparation and Caries Removal Laser‐Assisted Adhesion in Tooth Color Restoration Laser‐Activated Polymerization of Composite Resin Laser‐Assisted Removal of Restorative Materials and Metal Dowels Laser‐Based Management of Dentin Hypersensitivity Application of Lasers in Endodontics Application of Lasers to Diagnose Dental Pulp Health Laser‐Assisted Pulp Capping and Pulpotomy Laser‐Assisted Root Canal Disinfection and Shaping Laser‐Assisted Root Canal Obturation Laser‐Assisted Root Canal Retreatment Laser‐Assisted Apical Surgery Lasers in Periodontal Therapy Laser‐Assisted Periodontal Soft Tissue Management Laser‐Assisted Management of Periodontal Pocket Bactericidal Effect of Lasers on Periodontal Pocket Laser‐Assisted Calculus Removal Laser‐Assisted Management of Oral Hard Tissues Laser‐Assisted Management of Dental Hard Tissue Conclusion
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.047 | 0.020 |
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".