Phosphorus Transport in Simulated Snowmelt Water as Influenced by Landscape and Soil Properties
Bibliographic record
Abstract
Phosphorus movement is an environmental concern. The movement of P from surface soil and residues can cause P movement downward in the landscape into lower slopes and depressions through snowmelt runoff and tillage. This P can ultimately make its way into sensitive surface water bodies. Snowmelt is a significant contributor to P in runoff that occurs in many cold climate regions, including the northern Great Plains. However, there has been minimal research assessing how topography and landscape position relate to soil properties and snowmelt runoff P. Using spatial sampling and soil analysis (including measurements of P sorption capacities and available P) in conjunction with small-scale snowmelt simulations from four farm fields in southern Saskatchewan, key factors were identified associated with elevated P in simulated runoff. Within fields, areas that have been related to high runoff potential include depressions in landscapes that have greater slope gradients and accumulated lab ile P, and headlands where there is overlap of fertilizer application. In more gently undulating topography, a consistent dominant influence of landscape position was not evident, likely relating to very different topographical contexts of the prairie fields and multiple factors affecting mobilization of P to runoff waters. Overall, high available P levels in the soil were associated with higher P concentrations in snowmelt runoff. In addition, soil P sorption capacity had an inverse relationship with P in snowmelt. In summary, areas of the landscape that have higher soil labile P and low P sorption capacity are areas that have high runoff potential risk. While landscape position can be a good predictor of soil and runoff water labile P concentrations, especially landscapes with substantive topographical variation, other factors also mediate P contained in runoff. Assessments of soil labile P across a landscape are considered a good indicator of P content of runoff originating from that point in the landscape.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.005 | 0.001 |
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".