Ferric chloride amendment reduces phosphorus losses from flooded soil monoliths to overlying floodwater
Bibliographic record
Abstract
The accumulation of phosphorus (P) in agricultural soils and subsequent losses to waterways contribute to eutrophication in surface water bodies. In agricultural lands prone to prolonged flooding during spring snowmelt, P may be released to overlying floodwater and transported to lakes downstream. Ferric chloride (FeCl3) is a potential soil amendment to mitigate P losses, but its effectiveness for flooded soils with snowmelt is not well documented. Thirty-six intact soil monoliths taken from four agricultural fields in Manitoba's Red River Valley region were surface-amended with FeCl3 at three rates (0, 2.5, and 5 Mg ha–1) to evaluate the effectiveness of FeCl3 in minimizing P losses to porewater and floodwater. Over 8 weeks of simulated snowmelt flooding, porewater, and floodwater samples taken weekly were analyzed for concentrations of dissolved reactive P (DRP), calcium (Ca), magnesium (Mg), iron (Fe), manganese (Mn), and pH. Change in the redox potential was also measured weekly. With time of flooding, redox potential decreased in all soil monoliths. At early stages of flooding, the porewater pH values were significantly lower in FeCl3-amended monoliths but increased with flooding time. Porewater and floodwater DRP concentrations increased in all soils when flooded, but the magnitudes varied. Amendment of FeCl3 decreased the DRP concentrations from 17% to 97% in porewater and 26% to 99% in floodwater, with the effectiveness varying depending on the soil, FeCl3 rate, and flooding time. Amendment of FeCl3 increased porewater concentrations of Ca, Mg, Fe, and Mn. Soil amendment with FeCl3 at both rates shows promise in mitigating redox-induced P losses from flooded soils.
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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".