Arsenic Behavior in Paddy Fields during the Cycle of Flooded and Non-flooded Periods
Bibliographic record
Abstract
The behavior of As in paddy fields is of great interest considering high As contents of groundwater in several Asian countries where rice is the main staple. We determined the concentrations of Fe, Mn, and As in soil, soil water, and groundwater samples collected at different depths down to 2 m in an experimental paddy field in Japan during the cycle of flooded and non-flooded periods. In addition, we measured the oxidation states of Fe, Mn, and As in situ in soil samples using X-ray absorption near-edge structure (XANES) and conducted sequential extraction of the soil samples. The results show that Fe (hydr)oxide hosts As in soil. Arsenic in irrigation waters is incorporated in Fe (hydr)oxide in soil during the non-flooded period, and the As is quickly released from soil to water during the flooded period because of reductive dissolution of the Fe (hydr)oxide phase and reduction of As from As(V) to As(III). The enhancement of As dissolution by the reduction of As is supported by high As/Fe ratios of soil water during the flooded period and our laboratory experiments where As(III) concentrations and As(III)/As(V) ratios in submerged soil were monitored. Our work, primarily based on data from an actual paddy field, suggests that rice plants are enriched in As because the rice grows in flooded paddy fields when mobile As(III) is released to soil water.
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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".