Lip service: Histological phenotypes correlate with diet and feeding ecology in herbivorous pacus
Bibliographic record
Abstract
Complex prey processing requires the repositioning of food between the teeth, as modulated by a soft tissue appendage like a tongue or lips. In this study, we trace the evolution of lips and ligaments, which are used during prey capture and prey processing in an herbivorous group of fishes. Pacus (Serrasalmidae) are Neotropical freshwater fishes that feed on leaves, fruits, and seeds. These prey are hard or tough, require high forces to fracture, contain abrasive or caustic elements, or deform considerably before failure. Pacus are gape-limited and do not have the pharyngeal jaws many bony fishes use to dismantle and/or transport prey. Despite their gape limitation, pacus feed on prey larger than their mouths, relying on robust teeth and a hypertrophied lower lip for manipulation and breakdown of food. We used histology to compare the lip morphology across 14 species of pacus and piranhas to better understand this soft tissue. We found that frugivorous pacus have larger, more complex lips which are innervated and folded at their surface, while grazing species have callused, mucus-covered lips. Unlike mammalian lips or tongues, pacu lips lack any intrinsic skeletal or smooth muscle. This implies that pacu lips lack dexterity; however, we found a novel connection to the primordial ligament which suggests that the lips are actuated by the jaw adductors. We propose that pacus combine hydraulic repositioning of prey inside the buccal cavity with direct oral manipulation, the latter using a combination of a morphologically heterodont dentition and compliant lips for reorienting food.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".