Data from: Always chew your food: freshwater stingrays use mastication to process tough insect prey
Bibliographic record
Abstract
Chewing, characterized by shearing jaw motions and high-crowned molar teeth, is considered an evolutionary innovation that spurred dietary diversification and evolutionary radiation of mammals. Complex prey-processing behaviours have been thought to be lacking in fishes and other vertebrates, despite the fact that many of these animals feed on tough prey, like insects or even grasses. We investigated prey capture and processing in the insect-feeding freshwater stingray Potamotrygon motoro using high-speed videography. We find that Potamotrygon motoro uses asymmetrical motion of the jaws, effectively chewing, to dismantle insect prey. However, CT scanning suggests that this species has simple teeth. These findings suggest that in contrast to mammalian chewing, asymmetrical jaw action is sufficient for mastication in other vertebrates. We also determined that prey capture in these rays occurs through rapid uplift of the pectoral fins, sucking prey beneath the ray's body, thereby dissociating the jaws from a prey capture role. We suggest that the decoupling of prey capture and processing facilitated the evolution of a highly kinetic feeding apparatus in batoid fishes, giving these animals an ability to consume a wide variety of prey, including molluscs, fishes, aquatic insect larvae and crustaceans. We propose Potamotrygon as a model system for understanding evolutionary convergence of prey processing and chewing in vertebrates.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.252 | 0.160 |
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".