Determining Phosphorus Release Rates to Runoff from Selected Alberta Soils Using Laboratory Rainfall Simulation
Bibliographic record
Abstract
Phosphorus losses from agricultural land can cause accelerated eutrophication of surface water bodies. This study evaluated the use of soil test phosphorus (STP) levels to predict dissolved inorganic phosphorus (DIP) concentrations in runoff water from agricultural soils using laboratory rainfall simulation. The objectives of this study were to determine (i) to what extent STP concentrations can be used as a basis to predict P losses from Alberta soils and (ii) how extended rainfall simulation run times affected DIP losses. Soil samples collected from a total of 38 field sites, widely scattered throughout the southern half of Alberta, were subjected to rainfall simulation in the laboratory. The STP concentrations were determined using Miller-Axley, Norwest, Kelowna, Modified Kelowna Mehlich-III, and distilled water extraction methods. Each rainfall simulation event lasted for at least 90 min. Runoff samples were collected in time series for the duration of each simulation, during two distinct runoff intervals: (i) for the first 30 min of continuous runoff (T30) and (ii) for 40 min during runoff equilibrium (Teq). For all the STP extractants and both runoff intervals, the relationship with DIP-flow-weighted mean concentration (FWMC) was linear and highly significant with r2 values ranging from 0.74 to 0.96. However, the slopes of the resulting regression lines were, on average, 1.85 times greater for the T30 runoff interval over those computed for the Teq interval. Thus experimental methodology greatly influenced regression parameters, suggesting that more work was needed to verify these relationships under natural conditions. In addition, with many of the r2 values greater than 0.90 there would be little, if any, benefit derived by including soil properties in regression analysis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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 teacher head, 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".