Foraging patterns of juvenile walleye (<i>Stizostedion vitreum</i>) in a system consisting of a single predator and two prey species: testing model predictions
Bibliographic record
Abstract
We tested the hypothesis that the presence of a large number of energetically inferior prey (Daphnia pulex) in the environment with energetically superior prey (larval carp, Cyprinus carpio) interferes with foraging efficiency of juvenile walleye (Stizostedion vitreum). We monitored functional responses of juvenile walleye feeding on larval carp alone or in combination with D. pulex. When walleye were offered larval carp at increasing densities (10, 20, 50, and 100 individuals/30-L aquarium), they responded in accordance with a type-II functional response in 10-min trials. Walleye captured the maximum number of larval carp when offered a carp density of 20 individuals/aquarium without the daphnids. Further increase in density of larval carp had no effect on walleye foraging rate. The presence of D. pulex (900 individuals/aquarium) suppressed the efficiency of foraging walleye. The predator consistently captured fewer larval carp in all treatments when they were offered together with the daphnids even though walleye continued to select the more profitable larval carp. The negative effect of daphnids on the feeding rate is an example of environmental constraints interfering with optimal foraging models. In this study, walleye appeared to experience a confusion effect caused by the large number of inferior prey. Consequently, the predator could not gain as much energy per unit time when confronted with two prey types as it was when foraging on larval carp alone.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".