Ruminant mandibular tooth mesowear: a new scheme for increasing paleoecological sample sizes
Bibliographic record
Abstract
Abstract Extension of the mesowear method to include the lower cheek teeth of ruminants will dramatically increase sample sizes and thus the statistical power of paleodietary inferences. However, the mesowear method of Fortelius and Solounias, which was designed for application to the upper molars, does not effectively separate ruminant species by diet when applied to the lower teeth. Upper and lower mesowear scores have sometimes been compared among non‐analogous cusps (i.e. the buccal cusps of the maxillary teeth, which experience incursion and the buccal cusps of the mandibular teeth, which experience excursion during the chewing stroke). We therefore compare mesowear scores between the buccal cusps of maxillary cheek teeth and the lingual cusps of mandibular cheek for a large sample of ruminants because both cusps experience incursion during the chewing stroke. Using the original mesowear scoring method, we find dietary signal in both the maxillary and mandibular cheek teeth and a high correlation between them using both non‐phylogenetic and phylogenetic comparative methods. Noting unique patterns of mesowear among the mandibular teeth, we also propose a new scoring method with additional wear categories that improves dietary inference when applied to the lower teeth and is highly repeatable. We also find that mandibular mesowear scores are consistently lower than for their maxillary counterparts. Although differential wear among the upper and lower teeth is much less apparent when applying our new scoring method, wear differences might relate to anisodonty (i.e. mandibular cheek teeth are narrower). Overall, we recommend our new scoring method for application to the lingual cusps of the lower second molars of fossil ruminants.
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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 | 0.037 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.004 |
| 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".