Comparative assessment of algaecide performance on freshwater phytoplankton: Understanding differential sensitivities to frame cyanobacteria management
Bibliographic record
Abstract
Cyanobacterial bloom represent a growing threat to global water security. With fast proliferation, they raise great concern due to potential health and socioeconomic concerns. Algaecides are commonly employed as a mitigative measure to suppress and manage cyanobacteria. However, recent research on algaecides has a limited phycological focus, concentrated predominately on cyanobacteria and chlorophytes. Without considering phycological diversity, generalizations crafted from these algaecide comparisons present a biased perpective. To limit the collateral impacts of algaecide interventions on phytoplankton communities it is critical to understand differential phycological sensitivities for establishing optimal dosage and tolerance thresholds. This research attempts to fill this knowledge gap and provide effective guidelines to frame cyanobacterial management. We investigate the effect of two common algaecides, copper sulfate (CuSO4) and hydrogen peroxide (H2O2), on four major phycological divisions (chlorophytes, cyanobacteria, diatoms, and mixotrophs). All phycological divisions exhibited greater sensitivity to copper sulfate, except chlorophytes. Mixotrophs and cyanobacteria displayed the highest sensitivity to both algaecides with the highest to lowest sensitivity being observed as follows: mixotrophs, cyanobacteria, diatoms, and chlorophytes. Our results suggest that H2O2 represents a comparable alternative to CuSO4 for cyanobacterial control. However, some eukaryotic divisions such as mixotrophs and diatoms mirrored cyanobacteria sensitivity, challenging the assumption that H2O2 is a selective cyanocide. Our findings suggest that optimizing algaecide treatments to suppress cyanobacteria while minimizing potential adverse effects on other phycological members is unattainable. An apparent trade-off between effective cyanobacterial management and conserving non-targeted phycological divisions is expected and should be a prime consideration of lake management.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 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 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".