A Novel Method for Assessing Enamel Thickness Distribution in the Anterior Dentition as a Signal for Gouging and Other Extractive Foraging Behaviors in Gummivorous Mammals
Bibliographic record
Abstract
Gummivory poses unique challenges to the dentition as gum acquisition may often require that the anterior teeth be adapted to retain a sharp edge and to resist loading because they sometimes must penetrate a highly obdurate substrate during gum extraction by means of gouging or scraping. It has been observed previously that the enamel on the labial surface of the teeth used for extraction is thicker relative to that on the lingual surface in taxa that extract gums, while enamel is more evenly distributed in the anterior teeth of taxa that do not regularly engage in extractive behaviors. This study presents a quantitative methodology for measuring the distribution of labial versus lingual enamel thickness among primate and marsupial taxa in the context of gummivory. Computed microtomography scans of 15 specimens representing 14 taxa were analyzed. Ten measurements were taken at 20% intervals starting from the base of the crown of the extractive tooth to the tip of the cutting edge across the lingual and labial enamel. A method for including worn or broken teeth is also presented. Mann-Whitney U tests, canonical variates analysis, and between-group principal components analysis were used to examine variation in enamel thickness across taxa. Our results suggest that the differential distribution of enamel thickness in the anterior dentition can serve as a signal for gouging behavior; this methodology distinguishes between gougers, scrapers, and nonextractive gummivores. Gouging taxa are characterized by significantly thicker labial enamel relative to the lingual enamel, particularly towards the crown tip. Examination of enamel thickness patterning in these taxa permits a better understanding of the adaptations for the extraction of gums in extant taxa and offers the potential to test hypotheses concerning the dietary adaptations of fossil taxa.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".