Morphology does not predict performance: jaw curvature and prey crushing in durophagous stingrays
Bibliographic record
Abstract
All stingrays in the family Myliobatidae are durophagous, consuming bivalves and gastropods, as well as decapod crustaceans. Durophagous rays have rigid jaws, flat teeth that interlock to form pavement-like tooth plates, and large muscles that generate bite forces capable of fracturing stiff biological composites (e.g. mollusk shell). The relative proportion of different prey types in the diet of durophagous rays varies between genera, with some stingray species specializing on particular mollusk taxa, while others are generalists. The tooth plate module provides a curved occlusal surface on which prey is crushed, and this curvature differs significantly among myliobatids. We measured the effect of jaw curvature on prey-crushing success in durophagous stingrays. We milled aluminum replica jaws rendered from computed tomography scans, and crushed live mollusks, three-dimensionally printed gastropod shells, and ceramic tubes with these fabricated jaws. Our analysis of prey items indicate that gastropods were consistently more difficult to crush than bivalves (i.e. were stiffer), but that mussels require the greatest work-to-fracture. We found that replica shells can provide an important proxy for investigations of failure mechanics. We also found little difference in crushing performance between jaw shapes, suggesting that disparate jaws are equally suited for processing different types of shelled prey. Thus, durophagous stingrays exhibit a many-to-one mapping of jaw morphology to mollusk crushing performance.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".