Cyanobacteria biomass in shallow eutrophic lakes is linked to the presence of iron-binding ligands
Bibliographic record
Abstract
Iron (Fe) is an important regulator of phosphorus and nitrogen use efficiency by phytoplankton. We tested the prediction that pelagic cyanobacteria biomass in surface waters of shallow eutrophic lakes with low ferric ion concentration is linked to the presence, and potential utilization, of low molecular weight Fe-binding ligands. We sampled 30 lakes in Alberta, Canada, in 2012 for nutrients, cyanobacteria biomass, Fe-binding ligands (hydroxamates and catecholates), and toxins. Bioavailable ferric ion concentration (estimated as pFe) was significantly correlated to cyanobacteria biomass (curvilinear relationship, r2 = 0.45, P < 0.05). Nonmetric multidimensional models indicated that high cyanobacteria biomass corresponded to lakes with low ferric ion concentration (pFe ∼ 19), and regression tree analyses identified a threshold in ferric ion concentration (pFe = 22.1) that separated lakes with relatively low versus high cyanobacteria biomass. Where ferric ion concentration was low, hydroxamate-reactive compound concentration was positively correlated to cyanobacteria biomass. As the environment goes from higher to lower ferric ion concentration, the presence of cyanobacteria increases, and with further reduction of Fe, the environmental need for Fe-binding ligands becomes manifest.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".