Reducing Conditions Influence U(IV) Accumulation in Sediments during <i>In Situ</i> Bioremediation
Bibliographic record
Abstract
This study presents field experiments conducted in a contaminated aquifer in Rifle, CO, to determine the speciation and accumulation of uranium in sediments during in situ bioreduction. We applied synchrotron-based X-ray spectroscopy and imaging techniques as well as aqueous chemistry measurements to identify changes in U speciation in water and sediment in the first days follwing electron donor amendment. Limited changes in U solid speciation were observed throughout the duration of this study, and non-crystalline U(IV) was identified in all samples obtained. However, U accumulation rates strongly increased during in situ bioreduction, when the dominant microbial regime transitioned from iron- to sulfate-reducing conditions. Results suggest that uranium is enzymatically reduced during Fe reduction, as expected. Mineral grain coatings newly formed during sulfate reduction act as reduction hotspots, where numerous reductants can act as electron donors [Fe(II), S(II), and microbial extracellular polymeric substances] that bind and reduce U. The results have implications for identifying how changes in the dominant reducing mechanism, such as Fe versus sulfate reduction, affect trace metal speciation and accumulation. The outcomes from this study provide additional insights into uranium accumulation mechanisms in sediments that could be useful for the refinement of quantitative models describing redox processes and contaminant dynamics in floodplain aquifers.
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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 | 0.001 | 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".