Utility practices and perspectives on monitoring and source control of cyanobacterial blooms
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it