Residual benefits of alum, gypsum, and magnesium sulfate amendments in reducing phosphorus losses to snowmelt runoff
Bibliographic record
Abstract
• Snowmelt DRP concentration increased with the progression of snow melting. • Amendments were ineffective in reducing snowmelt DRP 18 months after application. • Snowmelt DRP was diluted with high snowmelt volumes, but DRP loads remained high. Phosphorus (P) in snowmelt runoff from agricultural fields across the Canadian prairies is a major source of pollution to freshwater bodies. Soil amendments have previously been shown to reduce P loss from soils in laboratory-simulated and field snowmelt studies; however, their residual benefits beyond one snowmelt event are unknown. This study examined the effectiveness of alum (Al 2 (SO 4 ) 3 ·18H 2 O), gypsum (CaSO 4 ·2H 2 O), and magnesium sulfate (MgSO 4 ·7H 2 O) in reducing P losses to snowmelt 18 months after amendment application. The study was conducted on a silty clay loam soil in Manitoba. Amendments were applied in the fall of 2020 at a rate of 2.5 Mg/ha. The amended treatments, including an unamended control with four replicates, were arranged in a randomized complete block design. Daily snowmelt was collected from each field plot in the spring of 2022, volume recorded, and analyzed for dissolved reactive P (DRP), pH, and cation concentrations. Snowmelt DRP concentrations increased over the sampling period regardless of treatment, with higher concentrations after the soils had thawed. In the latter days of sampling, the field plots that received amendments had snowmelt DRP concentrations 9 – 31 % lower than the control treatment, but the differences were not statistically significant. Snowmelt DRP loads showed a significant positive relationship with snowmelt volume but not with DRP concentration, suggesting that DRP load is largely controlled by the snowmelt volume rather than the snowmelt DRP concentration. Our results suggest that the amendments applied at this rate were ineffective in reducing DRP loads 18 months after application.
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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 | 0.000 | 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 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".