Mobility of arsenic and vanadium in waterlogged calcareous soils due to addition of zeolite and manganese oxide amendments
Bibliographic record
Abstract
Abstract Addition of manganese(IV) oxides (MnO2) and zeolite can affect the mobility of As and V in soils due to geochemical changes that have not been studied well in calcareous, flooded soils. This study evaluated the mobility of As and V in flooded soils surface‐amended with MnO2 or zeolite. A simulated summer flooding study was conducted for 8 weeks using intact soil columns from four calcareous soils. Redox potential was measured in soils, whereas pH, major cations, and As and V concentrations were measured biweekly in pore water and floodwater. Aqueous As and V species were modeled at 0, 4, and 8 weeks after flooding (WAF) using Visual MINTEQ modeling software with input parameters of redox potential, temperature, pH, total alkalinity, and concentrations of major cations and anions. Aqueous As concentrations were below the critical thresholds (<100 μg L−1), whereas aqueous V concentrations exceeded the threshold for sensitive aquatic species (2–80 μg L−1). MnO2‐amended soils were reduced to sub‐oxic levels, whereas zeolite‐amended and unamended soils were reduced to anoxic levels by 8 WAF. MnO2 decreased As and V mobilities, whereas zeolite had no effect on As but increased V mobility, compared to unamended soils. Arsenic mobility increased under anoxic conditions, and V mobility increased under oxic and alkaline pH conditions. Conversion of As(V) to As(III) and V(V) to V(IV) was regulated by MnO2 in flooded soils. MnO2 can be used as an amendment in immobilizing As and V, whereas the use of zeolite in flooded calcareous soils should be done cautiously.
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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".