From bacterial to thermochemical sulfate reduction: Sulfur isotope constraints on the genesis of hyper-enriched black shales
Bibliographic record
Abstract
Hyper-enriched black shales (HEBS) are thin stratiform and stratabound sulfide-rich (~70 vol%) units that have percentage level concentrations of metals such as Ni, Mo and Zn. In the Peel River area of Yukon, Canada, HEBS are highly enriched in Ni (~3 wt%), Zn (~0.2 wt%), and Mo (0.2 wt%) hosted mainly by millerite (NiS), sphalerite (ZnS) and pyrite (FeS 2 ). The processes that control millerite-sphalerite precipitation in HEBS are poorly constrained. Here, we report the first in-situ sulfur isotope measurements of sulfide minerals in the Peel River HEBS. Early diagenetic pyrite (δ 34 S -31.9 ± 9.0 ‰) formed in response to open-system bacterial sulfate reduction (BSR), whereas later sphalerite (16.5 ± 5.1 ‰) and millerite (+10.0 ± 1.4 ‰) crystallized via thermochemical sulfate reduction. We have developed a three-stage model for HEBS formation. Stage 1 involves high primary productivity, a redox-stratified water column, a low sedimentation rate, and an active Fe-oxide shuttle, which promoted BSR-driven pyrite precipitation. Organic matter scavenged Ni and Zn from the water column and pore-water, producing metal-rich kerogen. Stages 2 and 3 occur during late diagenesis, with the infiltration of an acidic, oxidized, metal-poor, sulfate-bearing brine that interacted with the metal-bearing kerogen, triggering thermochemical sulfate reduction and millerite-sphalerite precipitation. Subsequent fracturing and increased fluid-flow during peak hydrocarbon generation caused acid-driven replacement of sphalerite by a second generation of millerite. This model accounts for HEBS formation in both Canada and China, demonstrating that, although late-stage fluids did not transport the metals, brines were essential to their genesis.
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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) | 0.001 | 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".