How Does a Change in Labial Tooth Row Number Affect Feeding Kinematics and Foraging Performance of a Ranid Tadpole (<i>Lithobates sphenocephalus</i>)?
Bibliographic record
Abstract
Recent studies have explored feeding kinematics in tadpoles with intact labial teeth; however, it is unknown how missing teeth impacts foraging. We explored the impact of missing labial teeth on the feeding mechanics and foraging performance of Southern leopard frog (Lithobates sphenocephalus [= Rana sphenocephala]) tadpoles by controlling the pattern of labial tooth loss; that is, by surgically removing one row of labial teeth. We then used high-speed (500 frames/second) videography to test the hypothesis that tooth loss reduces the time that tadpoles attach to and graze upon an algal-covered substrate. We next conducted trials of foraging efficiency and foraging activity to test the hypothesis that tadpoles with fewer teeth forage less effectively than control tadpoles. The teeth of tadpoles from the surgery treatment slipped while closing and were in contact with an algal-covered substrate for a shorter duration compared to control tadpoles. Surprisingly, tadpoles with missing labial teeth obtained similar amounts of food and were as active as tadpoles with intact mouthparts. However, tadpoles with missing teeth completed about 25% more gape cycles per unit time than control tadpoles. Our data suggest that tadpoles with missing teeth compensate for inferior feeding kinematics during mouth closing in each gape cycle by increasing the number of gape cycles per unit time.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".