Does exposure to cues of fish predators fed different diets affect morphology and performance of Northern Leopard Frog (<i>Lithobates pipiens</i>) larvae?
Bibliographic record
Abstract
Phenotypic plasticity allows animals to change their morphological and life-history traits when exposed to predator cues, which modifies performance and can enhance survival but engender costs. Thus, the extent of plastic changes should vary in relation to the perceived risk of predation. We tested the hypothesis that plastic changes in morphology (and their effect on performance) and life history of developing Northern Leopard Frog (Lithobates pipiens (Schreber, 1782)) larvae vary when exposed to cues of fish predators fed different diets. During development, we exposed tadpoles to control cues, cues from brown bullhead (Ameiurus nebulosus (Lesueur, 1819)) fed trout pellets, or cues from A. nebulosus fed L. pipiens tadpoles. Tadpoles exposed to predatory fish cues had smaller bodies, deeper tail fins, slower growth and development rates, and better turning performance than tadpoles that were not exposed to predatory fish cues, but we found limited evidence that the predator’s diet had an effect on phenotypic plasticity. Predator diet affected tail morphology and activity, but the latter effect was only marginally significant. Lithobates pipiens tadpoles clearly respond to predatory fish cues, but it remains unclear whether their response is modulated by the predator’s diet.
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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 | 0.000 |
| 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".