Critical Factors Affecting Field-Scale Losses of Nitrogen and Phosphorus in Spring Snowmelt Runoff in the Canadian Prairies
Bibliographic record
Abstract
A long-term, field-scale study in southern Manitoba, Canada, was used to identify the critical factors controlling yearly transport of nitrogen (N) and phosphorus (P) by snowmelt runoff. Flow monitoring and water sampling for total and dissolved N and P were performed at the edge of field. The flow-weighted mean concentrations and loads of N and P for the early (the first half of yearly total volume of snowmelt runoff), late (the second half of yearly total volume of snowmelt runoff), and yearly snowmelt runoff were calculated as response variables. A data set of management practices, weather variables, and hydrologic variables was generated and used as predictor variables. Partial least squares regression analysis indicated that critical factors affecting the water chemistry of snowmelt runoff depended on the water quality variable and stage of runoff. Management practices within each year, such as nitrogen application rate, number of tillage passes, and residue burial ratio, were critical factors for flow-weighted mean concentration of N, but not for P concentration or nutrient loads. However, the most important factors controlling nutrient concentrations and loads were those related to the volume of runoff, including snow water equivalent, flow rate, and runoff duration. The critical factors identified for field-scale yearly snowmelt losses provide the basis for modeling of nutrient losses in southern Manitoba and potentially throughout areas with similar climate in the northern Great Plains region, and will aid in the design of effective practices to reduce agricultural nonpoint nutrient pollution in downstream waters.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".