Estimating Dissolved Reactive Phosphorus Concentration in Surface Runoff Water from Major Ontario Soils
Bibliographic record
Abstract
Phosphorus (P) loss from agricultural land in surface runoff can contribute to eutrophication of surface water. This study was conducted to evaluate a range of environmental and agronomic soil P tests as indicators of potential soil surface runoff dissolved reactive P (DRP) losses from Ontario soils. The soil samples (0- to 20-cm depth) were collected from six soil series in Ontario, with 10 sites each to provide a wide range of soil test P (STP) values. Rainfall simulation studies were conducted following the USEPA National P Research Project protocol. The average DRP concentration (DRP30) in runoff water collected over 30 min after the start of runoff increased (p < 0.001) in either a linear or curvilinear manner with increases in levels of various STPs and estimates of degree of soil P saturation (DPS). Among the 16 measurements of STPs and DPSs assessed, DPS(M3) 2 (Mehlich-3 P/[Mehlich-3 Al + Fe]) (r2 = 0.90), DPS(M3)-3 (Mehlich-3 P/Mehlich-3 Al) (r2 = 0.89), and water-extractable P (WEP) (r2 = 0.89) had the strongest overall relationship with runoff DRP30 across all six soil series. The DPS(M3)-2 and DPS(M3)-3 were equally accurate in predicting runoff DRP30 loss. However, DPS(M3)-3 was preferred as its prediction of DRP30 was soil pH insensitive and simpler in analytical procedure, ifa DPS approach is adopted.
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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) | 0.001 | 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".