Predicted impact of zebra mussel (<i>Dreissena polymorpha</i>) invasion on water clarity in Lake Mendota
Bibliographic record
Abstract
Lake Mendota, which is plagued by periodic cyanobacterial blooms, is typical of many lakes in the Laurentian Great Lakes region that are vulnerable to zebra mussels (Dreissena polymorpha) but have yet to be invaded. We coupled removal estimates with chlorophyll production estimates in a dynamic model to predict the likely impact of mussel-mediated removal of phytoplankton on water clarity across a range of hypothetical zebra mussel densities. Models were fit to chlorophyll and temperature data collected biweekly from Lake Mendota during 1977-1993. When we assumed daily epilimnetic circulation, the percentage of days when the chlorophyll concentration exceeded 50 µg·L-1 was decreased threefold at mussel densities as low as 1000 mussels·m-2. When we assumed less frequent epilimnetic circulation, the density of mussels required to substantially improve water clarity increased dramatically. We predict that zebra mussel invasion would lead to increased water clarity in Lake Mendota. Cyanobacterial blooms would be reduced but not eliminated. Negative impacts on other lake processes following zebra mussel invasion could outweigh the benefits of lower phytoplankton concentrations.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".