Cyanobacteria Predominance in Alberta's Eutrophic Lakes Linked to Iron Scavenging Strategy That Uses Siderophores and Toxins
Bibliographic record
Abstract
The role of the micronutrient iron in the regulation of cyanobacteria dominance and cyanotoxicity is poorly understood. Iron is required for important metabolic pathways, including both phosphorus (P) and nitrogen (N) assimilation, and low levels of this element may influence the assimilation of the macronutrients. The hypothesis tested was that cyanobacteria produce and utilize siderophores and toxins in low iron conditions to scavenge iron, when P and N are not limiting algal growth, providing some cyanobacteria with a competitive advantage over other algal species. Among the naturally eutrophic lakes studied, cyanobacteria were dominant at low iron (>pFe19) concentrations (Spearman r = 0.73, p=0.004). Under these low iron conditions, the concentration of hydroxamate siderophores was significantly related to cyanobacteria biomass (r2=0.81, p<0.001), and the concentrations of extracellular microcystin were significantly correlated to the concentrations of hydroxamate siderophores (r2=0.98, p<0.001). These findings provide support for iron regulation of cyanobacteria harmful algal blooms (cyanoHABs). Lake management programs can work to mitigate and prevent future cyanoHAB occurrences through the regulation of iron in naturally eutrophic lakes.
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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 | 0.001 | 0.001 |
| 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".