Response of yellow perch (<i>Perca flavescens</i>) in Oneida Lake, New York, to the establishment of zebra mussels (<i>Dreissena polymorpha</i>)
Bibliographic record
Abstract
We used long-term data on Oneida Lake, New York, to evaluate hypotheses about the effects of introduced zebra mussels (Dreissena polymorpha) on yellow perch (Perca flavescens). We detected no change in survival, diet, or numbers of young-of-the-year (YOY) yellow perch. YOY growth increased in association with zebra mussel introduc- tion and was marginally correlated with zooplankton size, which increased after zebra mussel introduction. Low num- bers of YOY in recent years did not explain their increased growth rate. The percentage of age 3 and older yellow perch that consumed zooplankton and benthos increased after zebra mussel introduction. Water clarity, which has increased since zebra mussel introduction, was inversely related to the percentage of the adult population with empty stomachs and positively related to the percentage that consumed benthos. The percentage of adult yellow perch that consumed zooplankton was positively related to zooplankton size. Despite the increase in percentage of adults consum- ing both types of invertebrate prey, we detected no changes in adult growth associated with zebra mussel introduction. This suggests that the principal effects of zebra mussels on yellow perch in Oneida Lake were not via benthic path- ways but through modifications of water clarity and zooplankton. Thus far, these effects have not been negative for the yellow perch population.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".