Nutrient loss from Saskatchewan cropland and pasture in spring snowmelt runoff
Bibliographic record
Abstract
Cade-Menun, B. J., Bell, G., Baker-Ismail, S., Fouli, Y., Hodder, K., McMartin, D. W., Perez-Valdivia, C. and Wu, K. 2013. Nutrient loss from Saskatchewan cropland and pasture in spring snowmelt runoff. Can. J. Soil Sci. 93: 445-458. To develop appropriate beneficial management practices (BMPs) for a watershed, it is essential to quantify the nutrients lost from agricultural fields and to identify the mechanisms of nutrient transport. To determine appropriate BMPs for a watershed in southeastern Saskatchewan, nutrient concentrations were measured in spring 2010 in snowmelt runoff from fertilized annual cropland (zero till) and perennial tame pastures. The majority of nutrient loss was in dissolved form, rather than as particulates. Significantly more nitrogen (N) was lost from pastures as dissolved ammonium than from cropland, while significantly more dissolved organic N was lost from croplands. Although there were no significant differences in total phosphorus (P) loss, there were significantly higher concentrations of dissolved reactive P in runoff from cropland, and significantly higher particulate P in runoff from pastures. Total carbon (C) in runoff was higher from cropland, due mainly to significantly higher dissolved organic C concentrations. Runoff from cropland contained significantly higher concentrations of dissolved potassium and sulfur, reflecting the fertilization of cropland fields with these nutrients. These preliminary results demonstrate that nutrients may be transported from agricultural lands by different mechanisms (e.g., in dissolved versus particulate forms) as a function of cropping system, requiring the development of specific types of BMPs to best control nutrient losses.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".