Optimizing land management strategies for maximum improvements in lake dissolved oxygen concentrations
Bibliographic record
Abstract
Eutrophication and anoxia are unresolved issues in many large waterbodies. Globally, management success has been inconsistent, highlighting the need to identify approaches which reliably improve water quality. We used a process-based model chain to quantify effectiveness of terrestrial nutrient control measures on in-lake nitrogen, phosphorus, chlorophyll and dissolved oxygen (DO) concentrations in Lake Simcoe, Canada. Across a baseline period of 2010-2016 hydrochemical outputs from catchment models INCA-N and INCA-P were used to drive the lake model PROTECH, which simulated water quality in the three main basins of the lake. Five terrestrial nutrient control strategies were evaluated. Effectiveness differed between catchments, and water quality responses to nutrient load reductions varied between deep and shallow lake basins. Nutrient load reductions were a significant driver of increased DO concentrations, however strategies which reduced tributary inflow had a greater impact on lake restoration, associated with changes in water temperature and chemistry. Importantly, when multiple strategies were implemented simultaneously, resultant large flow reductions induced warming throughout the water column. Negative impacts of lake warming on DO overwhelmed the positive effects of nutrient reduction, and limited the effectiveness of lake restoration strategies. This study indicates that rates of lake recovery may be accelerated through a coordinated management approach, which considers strategy interactions, and the potential for temperature change-induced physical and biological feedbacks. Identified impacts of flow and temperature on rates of lake recovery have implications for management sustainability under a changing climate.
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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 | 0.001 | 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".