Addressing Lake Erie eutrophication: An assessment of recent progress and recommendations
Bibliographic record
Abstract
Addressing eutrophication problems in the Great Lakes was a key motivating factor in the signing of the Great Lakes Water Quality Agreement by the U.S. and Canadian governments (the “Parties”) in 1972. In spite of progress in the intervening five decades, persistent eutrophication problems – including harmful algal blooms – continue to occur in western Lake Erie and in some embayments of most of the other Great Lakes. In 2020-2022 a work group of the International Joint Commission assessed progress in addressing nutrient loads in Lakes Erie (with a secondary emphasis on Lake Ontario). The project reviewed recent research relevant to addressing ongoing nutrient impacts in Lake Erie, reviewed and assessed progress under domestic action plans developed for Lake Erie, and the status of implementation of recommendations from previous International Joint Commission reports. The research review indicated significant progress in some areas, in particular concerning modeling, but ongoing needs in others, including on approaches to optimize appropriate best management practices at sufficient scales to reduce nutrient export from the land. The Parties' implementation of the Commission's recommendations has been mixed, with setting of targets and multiple modeling efforts on the one hand, but more limited work in other areas, including related to the economics of harmful algal blooms and on manure management. Our assessment of domestic action plans themselves, found both strengths (including in research, monitoring, adaptive management, and watershed-level planning), and limitations (including details on best management practices implementation, approaches to manure management, and funding needs for implementation). We identify multiple recommendations for strengthening programs in both countries, including through increased research addressing multiple aspects of best management practices effectiveness and implementation, consideration of an innovative approach such as group-level incentives to increase best management practices implementation in the agricultural community, and utilization of an accountability framework to advance progress towards meeting Lake Erie nutrient targets.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".