Alum and Gypsum Amendments Decrease Phosphorus Losses from Soil Monoliths to Overlying Floodwater under Simulated Snowmelt Flooding
Bibliographic record
Abstract
Phosphorus (P) loss from soils poses a threat of eutrophication to downstream waterbodies. Alum (Al2(SO4)3·18H2O) and gypsum (CaSO4·2H2O) are effective in reducing P loss from soils; however, knowledge on their effectiveness under cold temperatures is limited. This study examined the reduction of P loss from soils with alum and gypsum amendment under simulated snowmelt flooding. Intact soil monoliths (15 cm depth) collected from eight agricultural fields in flood-prone areas of Manitoba, Canada, were surface amended with alum or gypsum, pre-incubated for 2 weeks, then flooded and incubated at 4 °C for 8 weeks. Porewater and floodwater samples collected weekly were analyzed for dissolved reactive P (DRP), dominant cations and anions. An enhanced P release with flooding time was observed in all soils whether amended or unamended; however, alum/gypsum amendment reduced DRP concentrations in porewater and floodwater in general, with alum showing a more consistent effect across soils. The reduction in floodwater DRP concentrations (maximum DRP concentration during flooding) with alum and gypsum ranged from 34–90% and 1–66%, respectively. Based on Visual MINTEQ thermodynamic model predictions, precipitation of P and formation of P-sorbing mineral species with alum and gypsum amendment reduced DRP concentrations at latter stages of flooding.
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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) | 0.001 | 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".