In situ sulfur isotope analysis of pyrite from the Ezuri Kuroko‐type volcanogenic massive sulfide deposit, northeastern Japan: Contribution of microbial sulfate reduction to initial sulfide mineralization
Bibliographic record
Abstract
Abstract Volcanogenic massive sulfide (VMS) deposits are ancient analogues of seafloor massive sulfide (SMS) deposits. The importance and contribution of microbial activity during initial mineralization has recently been recognized in both VMS and SMS deposits. Here, we report in situ sulfur isotope compositions (δ34S) of pyrite from the Ezuri Kuroko‐type VMS deposit in northeastern Japan as determined by secondary ion mass spectrometry. During the evolutionary process of sulfide mineralization, pyrite textures changed from framboidal to colloform to euhedral. Initial framboidal pyrite had highly negative δ34S values down to −31.8‰ (average ± 1SD = −18.8‰ ± 13.0‰; n = 21), banded colloform pyrite exhibited medium δ34S values (−11.7‰ ± 10.4‰; n = 8), and euhedral pyrite displayed the highest average δ34S value of +2.7‰ ± 1.6‰ (n = 5); thus, δ34S varied as different textures of pyrite were produced during mineralization. The maximum isotopic fractionation between framboidal pyrite and past seawater sulfate (δ34S ca. +20‰) is −52‰; such values can be produced only by microbial sulfate reduction (MSR) in an open system. Framboidal pyrite with a low δ34S value is observed at the centers of sulfide‐rich areas within polished sections and has often been replaced by later sulfide minerals (sphalerite, galena, and chalcopyrite); thus, our S isotope data from the Ezuri pyrite reveal that sulfur derived from MSR induced and acted as a nucleation point for later sulfide mineral growth. Combined with previously reported data, our results endorse the importance and universality of MSR‐derived sulfur during the initial mineralization stage of both VMS and SMS deposits.
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 | 0.001 | 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 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".