Internal iron loading and warm temperatures are preconditions for cyanobacterial dominance in embayments along Georgian Bay, Great Lakes
Bibliographic record
Abstract
Previous work suggests that a high rate of internal ferrous iron (Fe2+) loading from anoxic sediments into overlying waters favours cyanobacteria dominance (>50% of the phytoplankton biomass) over eukaryotic algae. This Cyanobacteria–Ferrous conceptual model was assessed along the Georgian Bay coastline of Lake Huron, Ontario, in one meso-eutrophic and three oligotrophic embayments that experience natural hypolimnetic anoxia. Cyanobacteria dominated all embayments in the relatively warmer summer of 2012 but not in the much cooler summer of 2014, although hypolimnetic anoxia and internal Fe2+ loading were observed in both summers in all embayments. A cyanobacteria bloom large enough to turn the lake visibly green was observed only in warmer 2012 in the meso-eutrophic embayment. Results show that warm summer temperatures and internal Fe2+ loading are necessary preconditions for cyanobacteria dominance, while high nutrient levels are needed to form large blooms. There were no consistent patterns between dominance and total and dissolved phosphorus (P), total nitrogen, ammonium, and nitrate. Internal P loading was not a necessary precondition for dominance. While P removal programs will decrease phytoplankton biomass in eutrophic waters, oxidized surficial sediments must be maintained throughout an aquatic system to prevent cyanobacteria dominance.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".