Vertical mixing and weak stratification over zebra mussel colonies in western Lake Erie
Bibliographic record
Abstract
Zebra mussels ( Dreissena polymorpha ) are an invasive species that have been implicated in the reduction of algae stocks in the near‐shore environment of western Lake Erie. To determine their basin‐wide effects, we applied a two‐dimensional hydrodynamic and water‐quality model for 1994. The model accurately reproduced lake‐wide hydrodynamics and water quality. When modeled as true benthic organisms (resting on the bottom), the dreissenids grazed 53% of the western basin May through September net algal growth. This grazing resulted in a ~0.1‐mg L ‐1 reduction in the pelagic algae concentration relative to the case without dreissenids. In comparison, dreissenids grazed 77% western basin net algal growth when the lake was modeled as a fully mixed water column. We found that the biomass grazed was governed by a balance between the timescales of vertical wind‐induced mixing and benthic grazing. During calm conditions, weak diurnal stratification (~1°C between surface and bottom waters) was sufficient to suppress vertical mixing, when the mean daily wind speed 4 m above the lake surface ( U 4 ) was ~6 m s ‐1 . These conditions allowed a concentration boundary layer ~1 m thick to form, accounting for the reduced grazing effect relative to the fully mixed case. Entrainment of the concentration boundary layer occurred for U4>6 m s ‐1 (associated with the lake's characteristic 10‐d storm cycle) facilitating algae supply to the benthos. We formulated the mean daily biomass grazed in terms of the dreissenid areal pumping rate (a) and U 4 and found that because typically U 4 is ~6 m s ‐1 , the western basin is weakly stratified thermally and a concentration boundary layer forms when U 4 <3α or α>2 m 3 m ‐2 d ‐1 . The dynamics of both wind‐mixing and thermal stratification must, therefore, be considered in mixing models applied to shallow weakly stratified lake basins.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| 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 teacher head, 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".