Timing of Mouse Molar Formation Is Independent of Jaw Length Including Retromolar Space
Bibliographic record
Abstract
For humans and other mammals to eat effectively, teeth must develop properly inside the jaw. Deciphering craniodental integration is central to explaining the timely formation of permanent molars, including third molars which are often impacted in humans, and to clarifying how teeth and jaws fit, function and evolve together. A factor long-posited to influence molar onset time is the jaw space available for each molar organ to form within. Here, we tested whether each successive molar initiates only after a minimum threshold of space is created via jaw growth. We used synchrotron-based micro-CT scanning to assess developing molars in situ within jaws of C57BL/6J mice aged E10 to P32, encompassing molar onset to emergence. We compared total jaw, retromolar and molar lengths, and molar onset times, between upper and lower jaws. Initiation time and developmental duration were comparable between molar upper and lower counterparts despite shorter, slower-growing retromolar space in the upper jaw, and despite size differences between upper and lower molars. Timing of molar formation appears unmoved by jaw length including space. Conditions within the dental lamina likely influence molar onset much more than surrounding jaw tissues. We theorize that molar initiation is contingent on sufficient surface area for the physical reorganization of dental epithelium and its invagination of underlying mesenchyme.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".