Factors affecting the growth and condition of lake whitefish (Coregonus clupeaformis)
Bibliographic record
Abstract
Though declines in the growth and condition of Great Lakes lake whitefish ( Coregonus clupeaformis ) have been largely attributed to food web disruptions caused by invasive dreissenid mussels, a comprehensive evaluation of alternative hypotheses is currently lacking. Using various statistical approaches, we evaluated 69 years of data from the inner basin of South Bay, Lake Huron, considering the role of biological variables (food availability as Diporeia abundance and lake whitefish relative abundance as catch per unit effort, CPUE) versus environmental variables (climate change as growing degree days >5 °C and productive habitat capacity as percent epilimnetic volume, EV) on the condition and early growth rates of resident lake whitefish. Consistently, biological variables (Diporeia abundance, CPUE) best explained changes in lake whitefish growth and condition, respectively, in years when Diporeia data were available. In their absence, environmental variables (EV) best explained early growth rates of lake whitefish, whereas CPUE again best explained lake whitefish condition. Our analysis revealed that environmental change contributed significantly but alone was not sufficient to explain declines in lake whitefish growth after dreissenid establishment, whereas biological variables considered here could account for the majority of growth and condition changes observed in this population.
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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".