Nutrient indicators of agricultural impacts in the tributaries of a large lake
Bibliographic record
Abstract
AbstractLake Simcoe in Ontario, Canada, is a large lake surrounded by a mix of urban, agricultural, and less developed areas and is showing adverse effects from excess nutrient inputs, including low hypolimnetic oxygen concentrations. Knowledge of both the quantity and quality of nutrients and seston entering the lake is important because large reductions in phosphorus (P) loads have been proposed to help restore the lake and its coldwater fishery. We examined land use effects on P quality (i.e., bioavailability) and its relationship to seston in the tributaries of Lake Simcoe. Indicators of agricultural impacts were examined in 13 tributaries of Lake Simcoe, which were selected to represent a range of land use types. Bioavailability of P was assessed through analysis of different forms of P and stoichiometric indicators of nutrient status in seston. Nutrient sources were examined using the δ15N of seston. The percentage of cropland in the subwatershed had a strong relationship with P as reflected in higher soluble reactive P concentrations and lower indicators of P deficiency. Cropland land use effects were complicated; they contributed highly bioavailable P to a P deficient lake, and at the same time, contributed high seston loads causing turbidity, resulting in light deficiency. In the Lake Simcoe watershed, animal manure application on cropland could be a source of nutrients related to the δ15N variability and, correspondingly, bioavailable P. Management efforts should therefore include best management practices to reduce manure application to croplands and to prevent runoff from areas where manure is stored.Keywords:: agricultureLake Simcoeland usemanurenonpoint sources of phosphorusphosphorus limitationstreamsseston
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".