Impacts of Climate Change and Multiple Stressors on Water Levels and Phytoplankton in Small Temperate Lakes Within the Great Lakes Region Over Three Decades
Bibliographic record
Abstract
Changes in climate influence water quantity and water quality through hydrological processes, thermal regimes, and ice phenology. This thesis investigates the impacts of climate change and additional anthropogenic stressors on water quantity and quality in two study areas with minimal anthropogenic disturbance within the Great Lakes region. Between 1984 and 2014, water levels dropped by an average of 50 cm in northern Wisconsin lakes. We found that 49% of the variation in water levels was attributed to decreased precipitation, and 30% was attributed to warmer air temperatures. Water levels are projected to rise by an average of 44 cm by the year 2070. In south-central Ontario, phytoplankton dominance shifted from diatoms to chrysophytes between 1984 and 2013. Changes in lake chemistry and lake morphometry explained 60% of the variation in phytoplankton biomass. Understanding how multiple interacting stressors affect lakes will help improve ecosystem management strategies.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".