Differential Drawdown of Ammonium, Nitrate, and Urea by Freshwater Chlorophytes and Cyanobacteria<sup>1</sup>
Bibliographic record
Abstract
The chemical form of nitrogen (N) is deemed to be decisive in shaping the composition of the primary producer community. Recently, there has been a shift in the dominant form of N delivered to agricultural landscapes. Urea‐based fertilizers are a mainstay in modern agriculture, and their ubiquitous use has increased the likelihood of urea export to nearby freshwaters. The shift to urea fertilizers has coincided with the recent expansion of cyanobacteria harmful algal blooms (cyanoHABs). This study investigated N drawdown patterns between two major freshwater phytoplankton groups—chlorophytes and cyanobacteria. Experiments were designed to understand if different patterns of N drawdown occurred among taxa and the potential synergistic effects of multiple N substrates. Nitrate (NO3−), ammonium (NH4+), and urea were supplied in a series of paired combinations, and N concentrations were monitored to track N drawdowns. We did not find significant differences between phytoplankton classes when supplied with a single N substrate. However, we found that when N substrates were supplied in combination, significant differences in N drawdown patterns were observed. Urea was consumed more rapidly among cyanobacteria, being drawn down at significantly higher rates relative to inorganic N substrates. In contrast, inorganic N substrates were drawn down more rapidly among chlorophytes relative to urea. Our findings support the emerging urea–cyanoHAB link and the potential importance of urea in freshwater eutrophication. As society becomes increasingly dependent on urea for agricultural crops, the need to understand how urea influences phytoplankton community composition may be instrumental in predicting bloom dynamics.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".