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 F ortelius and S olounias, 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".