Snowmelt and its role in the hydrologic and nutrient budgets of prairie streams
Bibliographic record
Abstract
Small watersheds in the Canadian Prairies are characterized by seasonally disconnected hydrologic networks whereby stream channels are hydrologically connected during snowmelt but have disconnected reaches throughout the remainder of the year. Snowmelt is the most significant hydrological event in the Canadian Prairies, yet few studies have investigated the role of snowmelt in the nutrient budget of prairie streams. We quantified hydrologic and nutrient dynamics during snowmelt for ten agricultural subwatersheds distributed along a gradient of human activity in the Red River Valley, Canada, to evaluate the timing of nitrogen (N) and phosphorus (P) export. Elevated concentrations of total P (TP) and total N (TN) were observed during the snowmelt peak, with maximum concentrations reaching 3.23 mg TP L(-1) and 18.50 mg TN L(-1). Dissolved P and N dominated the total nutrient pool throughout snowmelt, likely due to reduced erosion and sediment transport resulting from the combination of the flat topography, frozen soil and stream banks, and gradual snow cover melt. Significant correlations were observed between snowmelt N load (r=0.91; p<0.05) and both agricultural land cover and fertilizer usage, with a weaker correlation between snowmelt P load (r=0.81; p<0.05) and agricultural area. Our results showed that snowmelt plays a key role in nutrient export to prairie aquatic ecosystems and this may have serious impacts on downstream ecosystems. Land use management practices need to consider the snowmelt period to control nutrient loads to Lake Winnipeg and other waterbodies in the Great Plains.
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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 | 0.000 | 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) | 0.000 | 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".