The effectiveness of cyanobacteria nitrogen fixation: Review of bench top and pilot scale nitrogen removal studies and implications for nitrogen removal programs
Bibliographic record
Abstract
One of the primary goals of eutrophication management of freshwater systems is to lower the risk of cyanobacterial blooms. This is typically accomplished at the watershed scale by placing controls on phosphorus (P) discharge from point sources. However, several researchers have questioned the predominance of the P management paradigm, arguing that dual nitrogen (N) and P controls would be more effective at preventing cyanobacteria blooms than P controls alone. This hypothesis is predicated in part on the hypothesis that if cyanobacteria are starved of N, which is an essential nutrient, cyanobacteria N2 fixation rates will not be high enough to maintain growth rates and biomass yields at or near previous levels. However, several single species cultures of heterocystous cyanobacteria directly examining the effect of removing N show that, when deprived of ammonium and nitrate, N2 fixing cyanobacteria compensate biochemically for the high energy cost of fixation when supplied with sufficient nutrients other than N. Biomass and growth rates were only slightly different under N2 than when grown under ammonium and nitrate, which is consistent with observations from the long-term experimental fertilization of Lake 227. Collectively, these bench top and pilot scale studies suggest that N control programs will not have a major impact on the magnitude of freshwater cyanobacteria blooms, although cyanobacteria species composition and toxin production might be affected.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".