Dentition Influences Shape of Oral Jaws in Fish
Bibliographic record
Abstract
The interactions between bone and tooth development are poorly understood and largely understudied. Although the teleost lower jaw (mandible) has been described morphologically, this information has not been quantified and the upper jaw (premaxilla) has largely been ignored in the literature. The purpose of this study is to understand how jaw shape and tooth presence/absence correlate with one another. We describe the development of the jaw bones of two related teleosts, one with extensive dentition (Mexican tetra; Astyanax mexicanus ) and the other without oral teeth (Zebrafish; Danio rerio ). We collected a growth series for each species, used an acid‐free double stain to visualise the skeleton, and then analyzed samples using outline shape analyses. Differences between species were observed throughout growth. Variation in bone shape was detected along the occluding edge of the premaxilla, as well as the rostral and caudal most regions of the mandible. In the premaxillae, this variation was statistically significant between adult Mexican tetra and zebrafish, while for the mandible only the caudal region had significant shape differences. This study provides insights into the potential cross talk between bone and tooth development, providing essential information in the field of vertebrate anatomy and evolution. This research was funded by the Natural Sciences and Engineering Research Council of Canada.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".