Iron and iron‐binding ligands as cofactors that limit cyanobacterial biomass across a lake trophic gradient
Bibliographic record
Abstract
Summary The frequency and intensity of cyanobacterial blooms (cyanoblooms) is increasing globally. While cyanoblooms in eutrophic (nutrient‐rich) freshwater lakes are expected to persist and worsen with climate change projections, many of the ‘new’ cyanobloom reports pertain to oligotrophic (nutrient‐poor) freshwater lakes with no prior history of cyanobloom occurrence. Iron (Fe) is required in nearly all pathways of cyanobacterial macronutrient use, although its precise role in regulating cyanobacterial biomass across a lake trophic gradient is not fully understood. In all lakes sampled representing a gradient in trophic status from oligotrophic to hypereutrophic (2.2–561.2 μg L−1 total phosphorus), the relative cyanobacterial biomass was highest at low predicted Fe bioavailability in eutrophic Alberta lakes (<1.0 × 10−22 mol L−1) and low Fe concentration in oligotrophic Ontario Lakes (<3.2 μg L−1). Fe‐binding organic ligands were measured within this range of low bioavailable Fe. Concentrations of ligands with reactive hydroxamate moieties were positively correlated to cyanobacterial biomass in lakes with low Fe bioavailability and supply, suggesting a possible cellular origin (i.e. siderophores) mediated by low Fe. These findings suggest that Fe serves as a possible cofactor that maintains cyanobacterial biomass across a lake trophic gradient and that cyanobacteria invoke a similar Fe‐scavenging system to overcome Fe limitation in lakes of all trophic states.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".