Utility practices and perspectives on monitoring and source control of cyanobacterial blooms
Bibliographic record
Abstract
Abstract Thirty‐five utilities across the United States (54%), Australia (26%), and Canada (20%) were surveyed to identify their experiences with early warning monitoring and source control of cyanobacteria. All utilities experience pelagic blooms, but only 20% monitor for benthic cyanobacteria. Most utilities (86%) have early warning monitoring programs. However, monitoring frequencies and long analytical turnaround times negatively impacted the effective use of monitoring data for rapid bloom detection and prompt implementation of reactive measures to control blooms/bloom‐related issues. Thus, a tiered monitoring approach is recommended: Tier 1–event detection, Tier 2–cyanobacteria confirmation, and Tier 3–metabolite confirmation. Most utilities (68%) implement source control strategies for cyanobacteria, with algaecides and aeration being the most frequently used (36%). Utilities relied on manufacturer recommendations to design source control strategies, although site‐specific optimization is needed based on water quality/bloom conditions. Control strategies were restricted by source geometry, limited optimization, metabolite generation, and environmental impacts. Successful source control of cyanobacteria was further negatively impacted by external nutrient loading. Therefore, source control strategies should be implemented jointly with external nutrient control initiatives.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 source (direct Gemma or distilled Codex), 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".