Assessing Nutrient Management Strategies to Control Harmful Algal Blooms in Lake Erie
Bibliographic record
Abstract
Harmful algal blooms (HAB) have impaired Lake Erie’s western basin water quality since the 1960s. Drivers of HABs are still the subject of debate and are likely the result of interactions among several biotic and abiotic factors. The problem is twofold: (1) uncertainty in the specific causes of HABs leads to inapt management solutions; and (2) managing a cross-boundary watershed requires collaboration and agreement on apt solutions from multiple stakeholders as well as many U.S. states and Canadian provinces. In this study, we use Bayesian hierarchical modeling (BHM) to investigate the relationships between nitrogen (N) and phosphorus (P) and phytoplankton biomass, cyanobacterial biomass, and microcystin concentration. We used both a within-lake and an across-lake approach and examined whether the inferences from western Lake Erie differ from the ones using multiple lakes across the country. We found that while P is still the primary driver of HABs in Western Lake Erie (WLE), the great variability between stations and months suggests that even within-lake, there may not be a single relationship characterizing phosphorus effects on HABs. We also interviewed 29 stakeholders actively involved in western Lake Erie’s watershed. We analyzed the stakeholders’ values, attitudes, and policy preferences to understand their differences or similarities and their effects on management decisions. We found that although stakeholders agree on the urgency of the problem, the different opinions and preferences of each interviewee may complicate the decision-making process in a highly collaborative watershed.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".