The trade-off between foraging activity and predation risk for <i>Rana temporaria</i> in different food environments
Bibliographic record
Abstract
We evaluated the trade-off between foraging activity and predation risk for larvae of an anuran species presented with different types of food resources. In the laboratory we examined the effects of benthic and phytoplankton resources and the two types combined on the activity and mortality of larvae of the common frog, Rana temporaria, exposed to predatory larva, Dytiscus marginalis. Predation mortality of tadpoles increased with the duration of the experiment and was highest in the presence of the phytoplankton resource alone. This was explained by a decrease in prey activity in the benthic- and combined-resource treatments when the predator was present, whereas in the phytoplankton treatment, the activity of the tadpoles were similar in the presence or absence of the predator. In the presence of the predator, prey mainly used the bottom of the aquarium in the benthic- and combined-resource treatments. In contrast, in the phytoplankton treatment they used the water column more than in the other treatments. In the presence of the predator, the prey had a lower consumption rate in the phytoplankton treatment than in the other treatments. Predator activity did not change among treatments. The results suggest that the trade-off between foraging activity and predation mortality for anuran larvae is strongly affected by the types of food resources present in the environment.
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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 | 0.001 | 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".