Modifiable Clinical Dental Impression Methods to Obtain Whole‐Mouth and Detailed Dental Traits From Vertebrates
Bibliographic record
Abstract
Dental impressions, developed for accurate capture of oral characteristics in human clinical settings, are seldom used in research on nonlivestock, nonprimate, and especially nonmammalian vertebrates due to a lack of appropriate tools. Studies of dentitions in most vertebrate species usually require euthanasia and specimen dissection, microCT and other scans with size and resolution tradeoffs, and/or ad-hoc individual impressions or removal of single teeth. These approaches prevent in-vivo studies that factor in growth and other chronological changes and separate teeth from the context of the whole mouth. Here, we describe a non-destructive method for obtaining high-resolution dentition-related traits that can be used on both living animals and museum specimens for almost all vertebrates, involving a customizable and printable dental impression tray. This method has repeatedly and accurately captured whole-mouth morphology and detailed features at high resolution in the living non-teleost actinopterygian fish, Polypterus senegalus, in a laboratory setting. It can be used for comparative morphology and to observe temporal changes such as the presence of microwear, tooth replacement rates, and occlusal and morphological changes through ontogeny.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.008 | 0.002 |
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".