Predicting Phosphorus Release from Anaerobic, Alkaline, Flooded Soils
Bibliographic record
Abstract
Anaerobic conditions induced by prolonged flooding often lead to an enhanced release of phosphorus (P) to floodwater; however, this effect is not consistent across soils. This study aimed to develop an index to predict P release potential from alkaline soils under simulated flooded conditions. Twelve unamended or manure‐amended surface soils from Manitoba were analyzed for basic soil properties, Olsen P (Ols‐P), Mehlich‐3 extractable total P (M3P ICP ), Mehlich‐3 extractable molybdate‐reactive P (M3P MRP ), water extractable P (WEP), soil P fractions, single‐point P sorption capacity (P 150 ), and Mehlich‐3 extractable Ca (M3Ca), and Mg (M3Mg). Degree of P saturation (DPS) was calculated using Ols‐P, M3P ICP or M3P MRP as the intensity factor, and an estimated adsorption maximum based on either P 150 or M3Ca + M3Mg as the capacity factor. To develop the model, we used the previously reported floodwater dissolved reactive P (DRP) concentration changes during 8 wk of flooding for the same unamended and manured soils. Relative changes in floodwater DRP concentration (DRP ratio ), calculated as the ratio of maximum to initial DRP concentration, ranged from 2 to 15 across ten of the soils, but were ≤1.5 in the two soils with the greatest clay content. Partial least squares analysis indicated that DPS3 calculated using M3P ICP as the intensity factor and (2 × P 150 ) + M3P ICP as the capacity factor with clay percentage can effectively predict DRP ratio ( r 2 = 0.74). Results suggest that P release from a soil to floodwater may be predicted using simple and easily measurable soil properties measured before flooding, but validation with more soils is needed. Core Ideas Phosphorus release from flooded soils to overlying water depended on soil properties. Relative increase in floodwater DRP concentration could be predicted using the model. Clay percentage and the degree of P saturation influenced P release from flooded soils.
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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 | 0.001 | 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 | 0.001 |
| 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 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".