Behaviour and growth of southern leopard frog (<i>Rana sphenocephala</i>) tadpoles: effects of food and predation risk
Bibliographic record
Abstract
I examined the effects of a nonlethal predator, larvae of the odonate Anax junius, and food-resource level on behaviour and growth of larval southern leopard frogs (Rana sphenocephala) to test whether the strength of effects of a nonlethal predator was influenced by background resource level. I crossed two levels of food resources, growth-limiting and non-growth-limiting, with the presence or absence of A. junius. Tadpoles responded to predators by altering spatial distribution and activity. When predators were present, tadpoles on both food treatments had similar low levels of activity, but tadpoles on the nonlimiting food treatment showed a much larger reduction in activity than tadpoles on the growth-limiting treatment. Tadpoles on both food treatments delayed metamorphosis when exposed to predators. Growth and size at metamorphosis were affected significantly by food treatment but not by predator treatment. However, the direction of response to predators differed. Tadpoles on the growth-limiting treatment were larger at metamorphosis and grew faster when exposed to predators; the opposite was true for tadpoles on the non-growth-limited food treatment. This raises the interesting possibility that for some species experiencing low resource availability, predators may induce a behaviourally mediated positive effect on growth.
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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".