A global scan of how the issue of nutrient loading and harmful algal blooms is being addressed by governments, non-governmental organizations, and volunteers
Bibliographic record
Abstract
Abstract Harmful algal blooms (HABs) in aquatic ecosystems are of concern worldwide. This review deals with how jurisdictions around the world are addressing this water quality issue to inform recommendations regarding nutrient loading and HABs in Missisquoi Bay-Lake Champlain and Lake Memphremagog; transboundary lakes located in the USA and Canada that suffer from symptoms of eutrophication. A global scan of the literature resulted in the consideration of 12 case studies of large water bodies within large watersheds, excluding in-lake geoengineering approaches. Although all of the systems experience excessive nutrient loading, they vary in two key ways: sources of nutrients and manifestations of eutrophication ranging from HABs, to limited recreational uses, to the additional complexity of internal loadings and fish kills, up to drinking water shutdowns. The case studies were analyzed with respect to four categories of approaches, namely: (i) regulatory; (ii) incentive-based; (iii) risk mitigation; and (iv) outreach, engagement, and educational. We found that the management frameworks are based on integrated watershed management planning and national standards. National water quality standards, however, are not stringent enough to prevent HABs. Overall, identified case studies did not successfully remediate HABs, they simply managed them.
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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 | 0.002 | 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".