Comparative effects of urea, ammonium, and nitrate on phytoplankton abundance, community composition, and toxicity in hypereutrophic freshwaters
Bibliographic record
Abstract
Dissolved nitrogen (N) as urea ([NH2]2CO), nitrate (NO‐3), and ammonium (NH+4) was added to naturally phosphorus (P)‐rich lake water (up to 175 µg P L‐1) to test the hypotheses that pollution of hypereutrophic lakes with N increases total algal abundance, alters community composition, and favors toxic cyanobacteria that do not fix atmospheric N2. Monthly experiments were conducted in triplicate in polymictic Wascana Lake, Saskatchewan, Canada, during July, August, and September 2008 using large (> 3140 liters) enclosures. Addition of all forms of N added at 6 mg N L‐1 increased total algal abundance (as chlorophyll a) by up to 350% relative to controls during August and September, when soluble reactive P (SRP) was > 50 µg P L‐1 and dissolved N : P was < 20 : 1 by mass. In particular, NH+4 and urea favored non‐heterocystous cyanobacteria and chlorophytes and NO‐3, urea promoted chlorophytes, some cyanobacteria, and transient blooms of siliceous algae, whereas N2‐fixing cyanobacteria and dinoflagellates exhibited little response to added N. Added N also increased microcystin production by up to 13‐fold in August and September, although the magnitude of response varied with N species and predominant algal taxon (Planktothrix agardhii, Microcystis spp.). These findings demonstrate that pollution with N intensifies eutrophication and algal toxicity in lakes with elevated concentrations of SRP and low N : P, and that the magnitude of these effects depends on the chemical form, and hence source, of N.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".