Deep Cyanobacteria Layers: An Overlooked Aspect of Managing Risks of Cyanobacteria
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide The risk of human exposure to cyanotoxins is partially influenced by the location of toxin-producing cyanobacteria in waterbodies. Cyanotoxin production can occur throughout the water column, with deep water production representing a potential public health concern, specifically for drinking water supplies. Deep cyanobacteria layers are often unreported, and it remains to be seen if lower incident rates reflect an uncommon phenomenon or a monitoring bias. Here, we examine Sunfish Lake, Ontario, Canada as a case study lake with a known deep cyanobacteria layer. Cyanotoxin and other bioactive metabolite screening revealed that the deep cyanobacteria layer was toxigenic [0.03 μg L –1 microcystins (max) and 2.5 μg L –1 anabaenopeptins (max)]. The deep layer was predominantly composed of Planktothrix isothrix (exhibiting a lower cyanotoxin cell quota), with Planktothrix rubescens (exhibiting a higher cyanotoxin cell quota) found at background levels. The co-occurrence of multiple toxigenic Planktothrix species underscores the importance of routine surveillance for prompt identification leading to early intervention. For instance, microcystin concentrations in Sunfish Lake are currently below national drinking water thresholds, but shifting environmental conditions (e.g., in response to climate change or nutrient modification) could fashion an environment favoring P. rubescens, creating a scenario of greater cyanotoxin production. Future work should monitor the entire water column to help build predictive capacities for identifying waterbodies at elevated risk of developing deep cyanobacteria layers to safeguard drinking water supplies.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".