Bibliographic record
Abstract
Introduction Eutrophication refers to enrichment of aquatic systems by inorganic plant nutrients (Wetzel, 1983; Mason, 1991). Lake eutrophication occurs when nutrient supplies, usually phosphorus (P) and nitrogen (N), are elevated over rates that occur in the absence of any system perturbation, and results in increased lake productivity. Causes of eutrophication include human (anthropogenic eutrophication) and non-human (natural eutrophication) disturbances. Marked natural eutrophication events are rare and may result from dramatic episodes, such as forest fire (e.g., Hickman et al ., 1990) and tree die-off (Boucherle et al ., 1986; Hall & Smol, 1993). Climatic shifts, such as droughts, may also concentrate lakewater nutrients or give rise to an increased contribution of nutrient-rich groundwater (e.g., Webster et al ., 1996). In most cases, however, water-quality problems are caused by anthropogenic nutrient inputs from domestic and industrial sewage disposal, farming activities and soil erosion. Eutrophication is the most widespread form of lake pollution on a global scale, and has many deleterious impacts on aquatic systems (Harper, 1992). In addition to increasing overall primary production, eutrophication causes considerable changes to biochemical cycles and biological communities. Marked changes occur at all levels in the food web and entire communities can change or die out. For example, changes in the ratio of N:P often results in primary production shifting from primarily diatoms and other smaller edible algae towards larger cyanobacteria that are better competitors for N (Tilman et al ., 1986), and more resistant to grazing (Reynolds, 1984).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".