Indirect effects of zebra mussels (<i>Dreissena polymorpha</i>) on the planktonic food web
Bibliographic record
Abstract
The exotic zebra mussel (Dreissena polymorpha) has caused dramatic reductions in phytoplankton density in lakes with dense mussel populations. However, the indirect effects of this invader on other trophic groups have been inconsistent and difficult to interpret. In some lakes, zebra mussels appear to have had little effect on zooplankton density, despite decreasing the abundance of their phytoplankton prey. We analyze food web models to test hypothesized mechanisms for the absence of a strong effect of dreissenids on zooplankton. Our results suggest that neither reduced inedible algal interference with zooplankton filtering nor reduced phytoplankton self-shading is sufficient to explain the insensitivity of zooplankton populations to dreissenid competition. Instead, we show how the impact of benthic filter feeders can be influenced by the rate of mixing within a basin, which limits phytoplankton delivery to the benthos. We explore the predictions of a simple spatially structured model and demonstrate that differences in abiotic factors that control mixing can result in large differences in direct and indirect effects of zebra mussel filtering.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".