The relationship between piscivory and growth of white sucker (<i>Catostomus commersoni</i>) and yellow perch (<i>Perca flavescens</i>) in headwater lakes of the Canadian Shield
Bibliographic record
Abstract
We used data from a survey of 36 headwater lakes of the Canadian Shield to investigate the relationship between piscivory and growth, abundance, and longevity of white sucker (Catostomus commersoni) and yellow perch (Perca flavescens). The occurrence of northern pike (Esox lucius) and walleye (Sander vitreus) explained variations in the abundance of both white sucker and yellow perch, suggesting strong predation-induced mortality. The longevity of both species tended to be negatively related to increased piscivory. White sucker grew better and had a better condition in lakes with piscivores. Yellow perch showed only small among-lake differences in growth and condition. The superior competitive ability of white sucker over yellow perch could explain why yellow perch did not show improved growth or longevity where population densities were low in lakes with piscivores and white sucker. Furthermore, yellow perch growth was inversely related to the biomass of piscivorous fish in their first year of life. Stomach content data suggest that small yellow perch, which rely on zooplankton, might restrict their use of pelagic resources to reduce their predation risk by piscivores, thus reducing their growth. Our results show that the effects of piscivores can be species-specific and dependent on community structure.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".