Relationships between Soil and Runoff Phosphorus in Small Alberta Watersheds
Bibliographic record
Abstract
Field-scale relationships between soil test phosphorus (STP) and flow-weighted mean concentrations (FWMCs) of dissolved reactive phosphorus (DRP) and total phosphorus (TP) in runoff are essential for modeling phosphorus losses, but are lacking. The objectives of this study were (i) to determine the relationships between soil phosphorus (STP and degree of phosphorus saturation (DPS)) and runoff phosphorus (TP and DRP) from field-sized catchments under spring snowmelt and summer rainfall conditions, and (ii) to determine whether a variety of depths and spatial representations of STP improved the prediction of phosphorus losses. Runoff was monitored from eight field-scale microwatersheds (2 to 248 ha) for 3 yr. Soil test phosphorus was determined for three layers (0 to 2.5 cm, 0 to 5 cm, and 0 to 15 cm) in spring and fall and the DPS was determined for the surface layer. Average STP (0 to 15 cm) ranged from 3 to 512 mg kg(-1), and DPS (0 to 2.5 cm) ranged from 5 to 91%. Seasonal FWMCs ranged from 0.01 to 7.4 mg L(-1) DRP and from 0.1 to 8.0 mg L(-1) TP. Strong linear relationships (r2=0.87 to 0.89) were found between the site mean STP and the FWMCs of DRP and TP. The relationships had similar extraction coefficients, intercepts, and predictive power among all three soil layers. Extraction coefficients (0.013 to 0.014) were similar to those reported for other Alberta studies, but were greater than those reported for rainfall simulation studies. The curvilinear DPS relationship showed similar predictive ability to STP. The field-scale STP relationships derived from natural conditions in this study should provide the basis for modeling phosphorus in Alberta.
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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 | 0.000 | 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) | 0.000 | 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".