Impact of zebra mussels (<i>Dreissena polymorpha</i>) on the pelagic lower trophic levels of Oneida Lake, New York
Bibliographic record
Abstract
We analyzed a data series on nutrients, phytoplankton, zooplankton, and young-of-the-year fish from Oneida Lake, New York, to test several hypotheses relating the response of the pelagic food web to grazing by zebra mussels (Dreissena polymorpha). System-wide grazing rates increased by one to two orders of magnitude after zebra mussel introduction. The most dramatic change associated with dreissenid grazing was increased water clarity and overall decrease in algal biovolume and Chl a. Contrary to predictions, primary production did not decline. We attribute the lack of whole water column decline in primary productivity to the compensating effect of increased water clarity resulting in deeper penetration of photosynthetically active radiation. We observed no change in total or dissolved phosphorus concentrations. Although algal standing crop declined, Daphnia spp. biomass and production did not, but dominance shifted from Daphnia galeata mendotae to Daphnia pulicaria. Consistent with our findings in the lower food web, we found no evidence that zebra mussels had a negative impact on young yellow perch (Perca flavescens) growth, biomass, or production. Thus, despite the order of magnitude increase in grazing rates and associated decrease in algal biomass, pelagic production at primary, secondary, and tertiary levels did not decline in association with zebra mussels.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".