Chromium Isotope Fractionation During Reduction of Cr(VI) Under Saturated Flow Conditions
Bibliographic record
Abstract
Chromium isotopes are potentially useful indicators of Cr(VI) reduction reactions in groundwater flow systems; however, the influence of transport on Cr isotope fractionation has not been fully examined. Laboratory batch and column experiments were conducted to evaluate isotopic fractionation of Cr during Cr(VI) reduction under both static and controlled flow conditions. Organic carbon was used to reduce Cr(VI) in simulated groundwater containing 20 mg L(-1) Cr(VI) in both batch and column experiments. Isotope measurements were performed on dissolved Cr on samples from the batch experiments, and on effluent and profile samples from the column experiment. Analysis of the residual solid-phase materials by scanning electron microscopy (SEM) and by X-ray absorption near edge structure (XANES) spectroscopy confirmed association of Cr(III) with organic carbon in the column solids. Decreases in dissolved Cr(VI) concentrations were coupled with increases in δ(53)Cr, indicating that Cr isotope enrichment occurred during reduction of Cr(VI). The δ(53)Cr data from the column experiment was fit by linear regression yielding a fractionation factor (α) of 0.9979, whereas the batch experiments exhibited Rayleigh-type isotope fractionation (α = 0.9965). The linear characteristic of the column δ(53)Cr data may reflect the contribution of transport on Cr isotope fractionation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".