Bibliographic record
Abstract
Tooth replacement is a common trait to most vertebrates, including mammals. Mammals, however, have lost the capacity for continuous tooth renewal seen in most other vertebrates, and typically have only 1–2 generations of teeth. Here, we review the mechanisms of tooth replacement in reptiles and mammals, and discuss in detail the current and historical theories on control of timing and pattern of tooth replacement and development. Researchers in Canada have explained the current theories on different tooth replacement patterns in vertebrate species. Like other vertebrates, the teeth we develop early in life are merely a ‘starter set’ that will ultimately be replaced. Mammals typically only undergo one round of replacement, while many fish and reptiles experience lifelong tooth replacement. Yet, the basis for this difference is not fully understood. In their review, John Whitlock and Joy Richman of the University of British Columbia, also present data suggesting that mammals may lose the dental lamina where tooth-forming stem cells reside, or fail to form the ‘successional lamina’ that contributes to later rounds of replacement in reptiles. The authors also explain mechanisms by which patterns of tooth renewal are established within the jaw, and recommend further study in reptile and mouse models.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".