Diet and Feeding Success of Fast‐Growing Yellow Perch Larvae and Juveniles in Perturbed Boreal Lakes
Bibliographic record
Abstract
Abstract The principal objective of this study was to test the hypothesis that enhanced early growth of yellow perch Perca flavescens in lakes affected by forest harvesting was related to favorable feeding conditions after the perturbation. Yellow perch larvae and juveniles and their zooplankton prey were sampled three times in three unperturbed lakes and in three perturbed lakes where forest harvesting had occurred in the catchment 2 years earlier. Univariate and multivariate analyses of the diets of age‐0 yellow perch from both treatments showed that fish in perturbed lakes primarily preyed upon Daphnia spp. and Polyphemus pediculus, whereas fish in unperturbed lakes preyed upon more diverse food items. Perturbed lakes showed higher dissolved organic carbon concentrations, algal biomass, and Daphnia spp. abundance. The feeding success index (number of prey items in the stomach per millimeter of fish length) and recent growth rates of age‐0 yellow perch showed a significant type II functional relationship with the abundance of Daphnia spp. We hypothesized that the increase in Daphnia spp. abundance and a darkening of water color in perturbed lakes may have favored prey detection and growth for larval and juvenile yellow perch, thereby affecting population recruitment.
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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 | 0.000 | 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) | 0.000 | 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".