Effects of Iron and Dissolved Organic Matter on Bioavailability of Arsenite under Anaerobic Conditions
Bibliographic record
Abstract
Understanding the effects of water chemistry on the availability of arsenic (As) to biota is important for predicting the environmental fate of As. The “dissolved” fraction of As (<0.22 μm) is often used as a proxy for bioavailable As. However, As speciation is also influenced by binding to dissolved organic matter (DOM) and colloidal iron (Fe) (oxy)hydroxides, which can impact bioavailability. Here, we use a recently developed Escherichia coli anaerobic biosensor to elucidate the effects of DOM and Fe on arsenite (As(III)) bioavailability under anaerobic conditions, where As can be highly mobile. Microbial As(III) uptake decreased with greater DOM and Fe(III) concentrations, while Fe(II) had no effect. Higher organic sulfur content in DOM was associated with decreased biouptake at low As(III)/C ratios, and X-ray absorption spectroscopy indicated that this was due to binding of As(III) to sulfur ligands like thiols. The 0.1–0.5 kDa size fraction of As was most closely related to the bioavailable As fraction. Because the aquaporin channels mediating As(III) uptake into both microbes and rice plants are structurally similar, our results may also have relevance for understanding of how biogeochemical conditions in rice paddies regulate the plant availability of arsenic.
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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) | 0.001 | 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".