Phosphorus mobilization in unamended and magnesium sulfate-amended soil monoliths under simulated snowmelt flooding
Bibliographic record
Abstract
Enhanced release of phosphorus (P) from soils with snowmelt flooding poses a threat of eutrophication to waterbodies in cold climatic regions. Reductions in P losses with various soil amendments has been reported, however effectiveness of MgSO4 has not been studied under snowmelt flooding. This study examined (a) the P release enhancement with flooding in relation to initial soil P status and (b) the effectiveness of MgSO4 at two rates in reducing P release to floodwater under simulated snowmelt flooding. Intact soil monoliths were collected from eight agricultural fields from Southern Manitoba, Canada. Unamended and MgSO4 surface-amended monoliths (2.5 and 5.0 Mg ha−1) in triplicates were pre-incubated for 7 days, then flooded and incubated (4 °C) for 56 days. Pore water and floodwater samples collected at 7-day intervals were analyzed for dissolved reactive P (DRP), pH, Ca, Mg, Fe and Mn. Redox potential (Eh) was measured on each day of sampling. Representative soil samples collected from each field were analyzed for Olsen and Mehlich 3-P. Simulated snowmelt flooding enhanced the mobility of soil P with approximately 1.2–1.6 -fold increase in pore water DRP concentration from 0 to 21 days after flooding. Mehlich-3 P content showed a strong relationship with the pore water DRP concentrations suggesting its potential as a predictor of P loss risk during prolonged flooding. Surface application of MgSO4 reduced the P release to pore water and floodwater. The 2.5 Mg ha−1 rate was more effective than the higher rate with a 21–75% reduction in average pore water DRP, across soils. Soil monoliths amended with MgSO4 maintained a higher Eh, and had greater pore water Ca and Mg concentrations, which may have reduced redox-induced P release and favored re-precipitation of P with Ca and Mg, thus decreasing DRP concentrations in pore water and floodwater.
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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 | 0.000 |
| 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".