How are the North American Great Lakes coping with multiple stressors? Comparison of Lakes Ontario and Superior
Bibliographic record
Abstract
The North American Great Lakes continue to be impacted by multiple stressors including: eutrophication and phosphorus abatement, invasive species, synergistic food web disruptions, degradation of fisheries and fish habitat, and climate change. The Great Lakes are an enormous global aquatic resource, spanning 245 000 km and containing 20% of the world’s supply of fresh water. Stressors affecting the health of the lakes have implications for the entire planet. In previous studies, we have considered the impact of exotic species on the complete food web of Lake Erie (Munawar et al. 2005) and the lower trophic levels of Lake Ontario (Munawar et al. 2006). Since these studies were published, more invasive species have been observed in the Great Lakes, including Hemimysis anomala in the summer of 2007 (J. Gerlofsma, Fisheries and Oceans Canada, pers. comm.). Lake Superior, perhaps due to its size (12 100 km) and relatively sparse population density along its shores, has not been subject to the same eutrophication pressure as Lake Ontario (1640 km; Vollenweider et al. 1974); however, both have been subject to ecosystemic disruption as a result of invasive species. In this study, we consider long-term changes in species composition at the top (fishes) and at the bottom (phytoplankton) of the food web of Lakes Ontario and Superior and consider the long term implications in terms for ecosystem health and resilience. We discuss how these synergistic changes reverberate through the food web to provide insights into the impact of multiple stressors for large lakes management.
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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) | 0.000 | 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".