Bibliographic record
Abstract
The management of eutrophication has been impeded by reliance on short-term experimental additions of nutrients to bottles and mesocosms. These measures of proximate nutrient limitation fail to account for the gradual changes in biogeochemical nutrient cycles and nutrient fluxes from sediments, and succession of communities that are important components of whole-ecosystem responses. Erroneous assumptions about ecosystem processes and lack of accounting for hysteresis during lake recovery have further confused management of eutrophication. I conclude that long-term, whole-ecosystem experiments and case histories of lake recovery provide the only reliable evidence for policies to reduce eutrophication. The only method that has had proven success in reducing the eutrophication of lakes is reducing input of phosphorus. There are no case histories or long-term ecosystem-scale experiments to support recent claims that to reduce eutrophication of lakes, nitrogen must be controlled instead of or in addition to phosphorus. Before expensive policies to reduce nitrogen input are implemented, they require ecosystem-scale verification. The recent claim that the 'phosphorus paradigm' for recovering lakes from eutrophication has been 'eroded' has no basis. Instead, the case for phosphorus control has been strengthened by numerous case histories and large-scale experiments spanning several decades.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".