Total phytoplankton abundance is determined by phosphorus input: evidence from an 18-month fertilization experiment in four subtropical ponds
Bibliographic record
Abstract
There is a heated debate over the necessity of nitrogen (N) reduction, in addition to phosphorus (P) reduction, for the control of eutrophication. Whole-lake fertilization experiments and lake restoration practices in high latitudes have demonstrated that P is the primary factor regulating total phytoplankton. Recognizing the limited large-scale evidence in warmer climatic zones, a fertilization experiment was conducted in four ponds located in the subtropical Yangtze River Basin, China. Total phytoplankton abundance in a pond receiving P (+P) was similar to that in a pond receiving both N and P (+N+P). Both had higher phytoplankton than a pond receiving no additional nutrient (Control). Total nitrogen concentration (TN) in the +P pond increased with the appearance of N-fixing cyanobacteria. Total phytoplankton abundance was similar in the ponds without P addition (+N, Control), and both ponds had lower phytoplankton levels than the +N+P pond. These results showed that P, not N, determines total phytoplankton abundance and that N deficiency is offset by N fixation in subtropical lakes. This experiment supports the idea that attention should be mainly focused on P reduction in mitigating eutrophication.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".