Low sediment redox promotes cyanobacteria blooms across a trophic range: implications for management
Bibliographic record
Abstract
Molot LA, Schiff SL, Venkiteswaran JJ, Baulch HM, Higgins SN, Zastepa A, Verschoor MJ, Walters D. 2021. Low sediment redox promotes cyanobacteria blooms across a trophic range: implications for management. Lake Reserv Manage. 37:120–142. Field observations and experimental manipulations with different oxidizing agents including nitrate demonstrate that high sediment redox prevents cyanobacteria blooms in eutrophic freshwaters. Conversely, low sediment redox caused by depletion of dissolved oxygen and nitrate allows blooms to form. This explains why bloom risk increases with phosphorus levels: Higher productivity increases the spatial and temporal extent of low sediment redox. The intermediate link between low redox and cyanobacteria blooms appears to be internal loading of ferrous iron (Fe2+) from reduced sediments with diffusion to depths accessible to migrating cyanobacteria, providing a source for their high iron demand. Regardless of whether Fe2+ release is the intermediate link, the concept of “low sediment redox as promoter” has major potential to improve bloom management if managers consider the impact of their nutrient management choices, nutrient targets, and in-lake methods on sediment redox. Phosphorus input targets can be adjusted as climate change alters the extent of anoxia, and short-term bloom prediction models that incorporate the sediment redox concept could predict onset of blooms earlier than current models that depend on detection of photosynthetic pigments associated with blooms.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".