Reactive Transport Modelling of Elevated Dissolved Sulphide Concentrations in Sedimentary Basin Rocks
Bibliographic record
Abstract
Groundwater with total dissolved sulphide concentrations in excess of 1.0 × 10 − 4 mol L − 1 3 mg L − 1 is relatively common at intermediate depths in sedimentary basins. However, the mechanisms responsible for the formation and spatial distribution of these sulphidic waters in sedimentary basins, which have been affected by periods of glaciation and deglaciation, are not fully understood. Sulphate reduction rates depend on many factors including redox conditions, salinity, temperature, and the presence and abundance of sulphate, organic matter, and sulphate-reducing bacteria. Two-dimensional reactive transport modelling was undertaken to provide potential explanations for the presence and distribution of sulphidic waters in sedimentary basins, partially constrained by field data from the Michigan Basin underlying Southern Ontario, Canada. Simulations were able to generally reproduce the observed depth-dependent distribution of sulphide. Sulphate reduction was most significant at intermediate depths due to anoxic conditions and elevated sulphate concentrations in the presence of organic matter in waters with relatively low salinity. The simulations indicate that glaciation-deglaciation periods increase mixing of waters at this interfacial zone, thereby enhancing rates of sulphate reduction and the formation of sulphide. In addition, the simulations indicate that glaciation-deglaciation cycles do not significantly affect sulphide concentrations in low permeability units, even at shallow depths (e.g., 25 m), while concentrations in permeable units remain stable below depths of 500 m.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".