Killing them softly: Ontogeny of jaw mechanics and stiffness in mollusk‐feeding freshwater stingrays
Bibliographic record
Abstract
Durophagous predators consume hard-shelled prey such as bivalves, gastropods, and large crustaceans, typically by crushing the mineralized exoskeleton. This is costly from the point of view of the bite forces involved, handling times, and the stresses inflicted on the predator's skeleton. It is not uncommon for durophagous taxa to display an ontogenetic shift from softer to harder prey items, implying that it is relatively difficult for smaller animals to consume shelled prey. Batoid fishes (rays, skates, sawfishes, and guitarfishes) have independently evolved durophagy multiple times, despite the challenges associated with crushing prey harder than their own cartilaginous skeleton. Potamotrygon leopoldi is a durophagous freshwater ray endemic to the Xingu River in Brazil, with a jaw morphology superficially similar to its distant durophagous marine relatives, eagle rays (e.g., Aetomylaeus, Aetobatus). We used second moment of area as a proxy for the ability to resist bending and analyzed the arrangement of the mineralized skeleton of the jaw of P. leopoldi over ontogeny using data from computed tomography (CT) scans. The jaws of P. leopoldi do not resist bending nearly as well as other durophagous elasmobranchs, and the jaws are stiffest nearest the joints rather than beneath the dentition. While second moment has similar material distribution over ontogeny, mineralization of the jaws under the teeth increases with age. Neonate rays have low jaw stiffness and poor mineralization, suggesting that P. leopoldi may not feed on hard-shelled prey early in life. These differences in the shape, stiffness and mineralization of the jaws of P. leopoldi compared to its durophagous relatives show there are several solutions to the problem of crushing shelled prey with a compliant skeleton.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".