Reactive Transport Modeling of Chromium Isotope Fractionation during Cr(VI) Reduction
Bibliographic record
Abstract
Chromium isotope fractionation is indicative of mass-transfer processes, such as reduction of Cr(VI) to Cr(III) during groundwater remediation. Laboratory experiments comparing batch and column treatment of Cr(VI) using organic carbon suggest that the associated isotope fractionation may be influenced by solute-transport mechanisms. These batch and column experiments were simulated using the reactive transport model MIN3P to further evaluate the effects of Cr reduction and transport on isotope fractionation under saturated flow conditions. Simulation of the batch experiment provided a good fit to the experimental data, where a fractionation factor (α₅₃) of 0.9965 was attributed to a single, dominant Cr(VI) removal mechanism. Calibration of the column simulations to the experimental results suggested the presence of a second, more rapid Cr(VI) removal mechanism with α₅₃ = 0.9992. Results from this study demonstrate that the interpretation of Cr isotope fractionation during reduction can be complex, particularly where multiple removal mechanisms are evident. Reactive transport modeling of Cr isotope fractionation can provide a quantitative assessment of the contaminant removal mechanisms, thus improving the application of Cr isotope measurements as a tool to track Cr(VI) migration and attenuation in groundwater.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".