Soil Testing to Predict Dissolved Reactive Phosphorus Loss in Surface Runoff from Organic Soils
Bibliographic record
Abstract
Phosphorus loss from surface runoff contributes to eutrophication of surface water, a problem that is often severe from polders with organic soils where agricultural production is intensive. A soil P test is essential to predict the potential for P losses to precisely conduct environmental risk assessment and to efficiently develop and evaluate beneficial management practices. This study evaluated the possibility of using the environmental and agronomic soil P tests, soil P sorption index (PSI), and degree of soil P saturation (DPS), which are used for mineral soils, to predict surface runoff dissolved reactive P (DRP) from organic soils. Forty‐four soils from eight subgroups representative of organic lands across Ontario were selected to provide a wide range of soil test P (STP) within each category. A surface runoff study was conducted following the U.S. National Phosphorus Research Project protocol. Flow‐weighted mean runoff DRP concentration (DRP 30 ) was linearly related to soil water‐ and CaCl 2 –extractable P concentrations but with data distribution patterns that inefficiently represented the soil variability in P release potentials. The runoff DRP 30 was significantly related to Bray‐1 P and FeO‐extractable P concentrations in split‐line models, each with a change point, but not to Mehlich‐3 P and Olsen P. All DPS values calculated based on STP and their derived PSIs were closely related to runoff DRP 30 in either a linear or a split‐line model. The DPS values expressed as Bray‐1 P/(PSI + Bray‐1 P) and FeO P/(PSI + FeO P) showed the highest correlation with runoff DRP 30 and thus can be recommended as environmental risk indicators of surface runoff DRP from organic soils.
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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 | 0.000 | 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 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".