Early diagenesis of sulfur in Bornholm Basin sediments: The role of upward diffusion of isotopically “heavy” sulfide
Bibliographic record
Abstract
Sediment-hosted marine sulfur cycling has played a significant role in regulating Earth’s surface chemistry over our planet’s history. Microbially-mediated reactions involving sulfur are often accompanied by sulfur isotope fractionation that, in turn, is captured by sulfate and sulfide minerals, providing the opportunity to track changes in the microbial utilization of sulfur and thus the marine sulfur cycle. Studying sulfur diagenesis within the Bornholm Basin, Baltic Sea, we explore the interplay between carbon, sulfur and iron, focusing on the fate of sulfur and the dynamics of the sulfur and oxygen isotopic response as a function of the varying thickness of the organic carbon-rich Holocene Mud Layer (HML) across the basin. Using a one-dimensional reaction-transport model, porewater sulfate and sulfide profiles were used to calculate net sulfate reduction rates (SRR) and net sulfide production rates, respectively. These calculations suggest a positive relationship between the thickness of the HML and net rates of sulfate reduction and sulfide production. Given that ascending sulfide is enriched in 34S relative to that produced in-situ, a heightened sulfide flux promotes spatially variable precipitation of 34S-enriched pyrite (δ34S ≈ −10‰) close to the sediment–water interface. Modeling results indicate that this isotopically “heavy” sulfide is formed as a consequence of mixing between ascending sulfide (up to +6.3‰) and that produced in-situ (ca. −40‰). Further, we show that the sulfur and oxygen isotopic composition of porewater sulfate is controlled by the net SRR: when the net SRR is high (i.e., in sulfide-replete settings) the downcore increase in δ18OSO4 is dampened relative to increase in δ34SSO4, whereas when net SRR is low (i.e., in iron-rich parts of the basin) downcore δ18OSO4 values increase while δ34SSO4 values remain invariant. We conclude that sedimentation rates and open system diffusion strongly influence the distribution of sulfur species and their sulfur isotopic composition, as well as the oxygen isotopic composition of sulfate, through the interaction between iron, sulfur and methane. This work highlights the importance of considering diffusion to better understand open system diagenesis and the δ34S signatures of sulfate and sulfide in both modern settings and ancient rocks.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".