Growth, ingestion rates and metabolic activity of walleye in lakes with and without lake herring
Bibliographic record
Abstract
Growth efficiencies, ingestions rates and activity levels of walleye Sander vitreus were compared in lakes with and without lake herring Coregonus artedi. Yellow perch Perca flavescens were the main prey in lakes without lake herring. Walleye were sampled in September and October from 38 lakes in Ontario in 1998 and 1999, using multimesh monofilament gillnets. Ingestion rates were estimated from annual increments in somatic mercury and body mass, and the mercury content of yellow perch and lake herring. Walleye had higher growth efficiencies, and lower ingestion and activity rates in lakes with lake herring. Lake herring grow larger than yellow perch and therefore could provide more profitable prey for larger walleye. The results are consistent with optimal foraging theory that predicts that walleye feeding on optimal prey sizes should grow more efficiently, if the ratio of feeding benefit (energy) to cost (search and seizure) is a function of the ratio of predator and prey size.
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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".