Uranium Isotope Fractionation during Adsorption, (Co)precipitation, and Biotic Reduction
Bibliographic record
Abstract
Uranium contamination of surface environments is a problem associated with both U-ore extraction/processing and situations in which groundwater comes into contact with geological formations high in uranium. Apart from the environmental concerns about U contamination, its accumulation and isotope composition have been used in marine sediments as a paleoproxy of the Earth’s oxygenation history. Understanding U isotope geochemistry is then essential either to develop sustainable remediation procedures as well as for use in paleotracer applications. We report on parameters controlling U immobilization and U isotope fractionation by adsorption onto Mn/Fe oxides, precipitation with phosphate, and biotic reduction. The light U isotope ( 235 U) is preferentially adsorbed on Mn/Fe oxides in an oxic system. When adsorbed onto Mn/Fe oxides, dissolved organic carbon and carbonate are the most efficient ligands limiting U binding resulting in slight differences in U isotope composition (δ 238 U = 0.22 ± 0.06‰) compared to the DOC/DIC-free configuration (δ 238 U = 0.39 ± 0.04‰). Uranium precipitation with phosphate does not induce isotope fractionation. In contrast, during U biotic reduction, the heavy U isotope ( 238 U) is accumulated in reduced species (δ 238 U up to −1‰). The different trends of U isotope fractionation in oxic and anoxic environments makes its isotope composition a useful tracer for both environmental and paleogeochemical applications.
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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".