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), Mehlich-3 extractable molybdate-reactive P (M3P), water extractable P (WEP), soil P fractions, single-point P sorption capacity (P), and Mehlich-3 extractable Ca (M3Ca), and Mg (M3Mg). Degree of P saturation (DPS) was calculated using Ols-P, M3P or M3P as the intensity factor, and an estimated adsorption maximum based on either P 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), 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 as the intensity factor and (2 × P) + M3P as the capacity factor with clay percentage can effectively predict DRP ( = 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.
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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) | 0.001 | 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".