Comparative feeding kinematics of tropical hylid tadpoles
Bibliographic record
Abstract
Anuran larvae, which are otherwise simple in shape, typically have complex keratinized mouthparts (i.e. labial teeth and jaw sheaths) that allow them to graze upon surfaces. The diversity in these structures among species presumably reflects specializations that allow for maximal feeding efficiency on different types of food. However, we lack a general understanding of how these oral structures function during feeding. We used high-speed digital imaging (500 Hz) to observe tadpoles of six species from the anuran family Hylidae grazing on a standardized food-covered substrate. Tadpoles of these species vary in the number of labial tooth rows, belong to two different feeding guilds (benthic and nektonic), and inhabit ponds and streams. We confirmed that the labial teeth in these species serve two functions: anchoring the mouth to the substrate and raking material off of the substrate. In general, tadpoles with a larger maximum gape or those with fewer labial tooth rows opened and closed their mouths slower than tadpoles with smaller gape or more tooth rows. Nektonic feeding tadpoles released each of their tooth rows proportionally earlier in the gape cycle compared with benthic feeding tadpoles. Lastly, we found some support for the idea that deformation of the jaw sheaths during a feeding cycle is predictable based on tadpole feeding guild. Collectively, our data show that anatomical (e.g. number of labial teeth) and ecological features (e.g. feeding guild) of tadpoles significantly influence how tadpoles open and close their mouths during feeding.
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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 | 0.000 |
| 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".