<i>Oryza sativa</i> as a Tool for Assessing Arsenic Efficacy of Arsenic Remediation of Agricultural Soils by Sulfidated Zerovalent Iron Nanoparticles
Bibliographic record
Abstract
Arsenic (As) is highly toxic in its inorganic form. It is naturally presented at elevated levels in the groundwater of a number of countries and contaminates drinking water sources, generating numerous health and environmental problems. Current methodologies for its remediation have deficiencies which fuel the constant exploration of new alternatives. Therefore, the development of robust methodologies for the evaluation of potential remediation technologies are not only timely but also highly needed. In this study we have investigated the use of a rice plant species as a means to evaluate the efficacy of As remediation using sulfidated zerovalent iron nanoparticles (S-nZVI). The obtained results show that addition of S-nZVI to soils had a beneficial impact to plant growth in the presence of As(V) and As(III) concentrations between 10 and 50 ppm. Positive effects were also found for plant biomass and chlorophyll content in the plants. Moreover, evaluation of As uptake by plants showed that the application of S-nZVI reduced the amount of both As(V) and As(III) in shoots and increased the amount of As in the roots. Studies on the Fe and P content in shoot and root after exposure to As with and without the nanoparticles demonstrated that nanoparticles remain mainly in the roots and that P uptake by plants was not significantly affected, suggesting that S-nZVI treatment is safe for plants at the assayed doses. These results overall confirm the method as robust and reliable for demonstrating the reduction of the bioavailability of As in soil by S-nZVI sequestration.
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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) | 0.001 | 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".