Seasonal Influence on Subsurface Rates of Microbial Sulfate Reduction and Sulfur Isotope Fractionation in Coastal Sediments
Bibliographic record
Abstract
Abstract Sulfur isotope fractionation during microbial sulfate reduction is often preserved in the mineral pyrite (FeS 2 ), which has been used to reconstruct the biogeochemical sulfur cycle and redox geochemistry of the oceans over the Earth history. Understanding what controls the preserved sulfur isotopic composition of pyrite is therefore of paramount importance, but it has been difficult to deconvolve the influence of environmental changes from changes in sedimentation rate. We present a 16‐month record of pore fluid geochemical profiles with in situ sampling apparatus installed in coastal sediments, one of which is dominated by microbial sulfate reduction and the other dominated by bacterial iron reduction. Our data include monthly sulfate (SO 4 2− ) and chloride concentrations (Cl − ), dissolved iron concentrations (Fe 2+ ), and the sulfur isotopic composition of dissolved sulfate (δ 34 S SO4 ) up to 36 cm below the sediment‐water interface. We use a reactive transport model to determine the expressed sulfur isotopic fractionation factor for each month and a Monte Carlo simulation to calculate net sulfate flux into the sediment based on pore fluid profiles from the sulfidic sediment. Net rates of sulfate reduction in the sulfidic sediment vary by three orders of magnitude over the seasonal cycle and are positively correlated with air temperature. The expressed sulfur isotope fractionation factor varies between 20 and 70‰ and reaches the thermodynamic limit in the colder months. Our data suggest that the correlation between temperature and the subsurface microbial sulfur biogeochemical cycle should be considered when interpreting sulfur isotope ratios in pyrite over Earth history.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".