<sup>34</sup>S/<sup>32</sup>S Fractionation during Sulfate Reduction in Groundwater Treatment Systems: Reactive Transport Modeling
Bibliographic record
Abstract
Isotope ratio measurements provide a tool for indicating the relative significance of biogeochemical reactions and for constraining estimates of the extent and rate of reactions in passive treatment systems. In this paper, the reactive transport model MIN3P is used to evaluate sulfur isotope fractionation in column experiments designed to simulate treatment of contaminated water by microbially mediated sulfate reduction occurring within organic carbon-based and iron and carbon-based permeable reactive barriers. A mass dependent fractionation model was used to determine reaction rates for 32S and 34S compounds during reduction, precipitation, and dissolution reactions and to track isotope-dependent mass transfer during SO4 removal. The δ34S values obtained from the MIN3P model were similar to those obtained from the Rayleigh equation, indicating that there was not a significant difference between the conceptual models. Differences between the MIN3P derived α value and the Rayleigh equation derived value were attributed to minor changes in the dissolution and precipitation rate of gypsum and mathematical differences in the fitting models. The results indicated that the prediction of δ34S was fairly insensitive to differences in the fractionation factor at the concentration ranges measured in the current study. However, more significant differences would be expected at low sulfate conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".