Muscle enzymes reveal walleye (<i>Sander vitreus</i>) are less active when larger prey (cisco,<i>Coregonus artedi</i>) are present
Bibliographic record
Abstract
Optimal foraging behaviour in walleye (Sander vitreus) was tested in eight lakes: four containing large prey (cisco, Coregonus artedi) and four without cisco. All eight lakes contained small prey (yellow perch, Perca flavescens). Activity costs and growth potential of walleye were estimated using white muscle enzymes. Anaerobic capacity, measured by lactate dehydrogenase (LDH), increased with walleye size and was lower in lakes with cisco. Aerobic capacity, measured by citrate synthase (CS), decreased with walleye size only in lakes without cisco. Growth potential of walleye, estimated by nucleoside diphosphokinase (NDPK), increased with size only in lakes with cisco. Overall, when cisco were available walleye were less active, able to maintain aerobic capacities, and increased their growth potential as they grew larger. Yellow perch LDH, CS, and NDPK were lower in lakes with cisco. This suggests that yellow perch were less active in lakes where walleye had an alternative large prey species, but yellow perch had higher growth potential when they were the only prey, reflecting the advantage of growth beyond edible sizes. This study reveals that there are physiological benefits for both predators and prey in communities with a wider range of prey sizes.
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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".