Understanding sorption mechanisms of uranium onto elemental iron, minerals and Shewanella putrefaciens surfaces in the presence of arsenic
Bibliographic record
Abstract
The concomitant occurrence and reported discrepant behavior of uranium and arsenic in water bodies is a major health and environmental concern. This study combined batch and column experiments, hydrogeochemical simulations and XAFS spectroscopy to uncover the exchange mechanisms governing uranium fate between water and scrap metallic iron, minerals and Shewanella putrefaciens surfaces in the presence of arsenic. The main results suggest that both water chemistry and the solid phase composition influence uranium fate in the presence of arsenic. The importance of uranyl-arsenate species as a major control of uranium behavior in the presence of arsenic is shown. The toxicity of arsenic and the presence of nitrate are interpreted as limiting factors of the enzymatic reduction of both toxins. Besides, XANES fingerprinting and EXAFS modeling have confirmed precipitation/co-precipitation of uranyl-arsenates as a major mechanism controlling uranium behavior in the presence 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".