Experimental evidence that pollution with urea can degrade water quality in phosphorus‐rich lakes of the Northern Great Plains
Bibliographic record
Abstract
Urea is the most abundant nitrogen (N) fertilizer used on agricultural soils, yet its effects on adjacent aquatic ecosystems are largely unknown. Here 21‐d, 3000‐liter mesocosm experiments were conducted monthly in a hypereutrophic lake during July–September 2007 to quantify how addition of urea might affect phytoplankton abundance, gross community composition, and algal toxicity in a phosphorus (P)‐rich lake. Repeated measures analysis of variance demonstrated that addition of sufficient urea to increase ratios of soluble N: P from ~ 15 : 1 to > 24 : 1 (by mass) also increased algal biomass (as Chlorophyll ά) and microcystin concentrations 200–400%, as non–N2‐fixing but toxic cyanobacteria (Microcystis, Planktothrix) and less harmful chlorophytes (Micractinium, Oocystis) replaced colonial N2‐fixing cyanobacteria (Anabaena, Aphanizomenon). No significant effects of urea amendment were recorded for trials in which N: P ratios were elevated at the start of the experiment, or in which ambient light levels were reduced to 25 µmol quanta m−2 s−1, although preliminary evidence suggests that urea addition stimulated growth of heterotrophic bacteria irrespective of light regime. Development of toxic non–N2‐fixing cyanobacteria by N pollution of P‐rich lakes is consistent with findings from whole‐lake experiments and paleolimnological studies of deep lakes, and suggests that the fertilization needed to feed 3 billion more people by 2050 may create conditions in which future water quality in P‐replete regions is degraded further by urea export from farms and cities.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".