Tooth microwear texture in odontocete whales: variation with tooth characteristics and implications for dietary analysis
Bibliographic record
Abstract
Understanding the diets and trophic relationships of toothed whales is central to understanding their roles in marine ecosystems, and associated conservation issues. Yet this is problematic because direct observation of what free ranging marine mammals eat is difficult. Quantitative 3D textural analysis of tooth microwear (DMTA) offers a new way of investigating diet in odontocetes and other marine mammals, but the application of this approach requires that we first understand how non-dietary variables affect the texture of microwear in odontocetes. Here we present the first analysis of microwear texture in odontocetes (beluga, Delphinapterus leucas) testing null hypotheses that microwear texture does not vary with dental surface tissue type (dentine, cementum), and that microwear texture does not vary with tooth characteristics (location in jaw, degree of wear, wear facet slope and facet orientation). Our results reveal that these variables have a significant impact on microwear textures, and thus have the potential to mask variation in texture caused by dietary differences. This does not mean that microwear texture analysis cannot be used as a tool for dietary analysis in toothed whales, but any future studies should adopt sampling protocols that standardize non-dietary variables to mitigate their effects in DMTA analysis.
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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 | 0.001 | 0.001 |
| 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 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".