Greenalite as a record of hydrothermal input and ocean anoxia, Late Devonian Xialei Mn deposit, South China
Bibliographic record
Abstract
Abstract Greenalite in iron-rich Precambrian sedimentary rocks is widely regarded as a primary mineral that formed in ferrous iron-rich and anoxic conditions. However, primary greenalite has rarely been reported in Phanerozoic rocks, as Phanerozoic oceans are considered to have been more oxygenated and less favorable for greenalite precipitation. Here, we report the discovery of greenalite nanoparticles in the Late Devonian Xialei Mn deposit. Nanoscale (20–1000 nm) euhedral to anhedral greenalite particles occur as randomly oriented crystallites enclosed in microcrystalline quartz crystals within chert-rich Mn and Fe-Mn ores. Greenalite-bearing ores show negative δ30Si values (avg. −0.5‰) close to those of Precambrian iron formations and hydrothermal silica, suggesting a substantial hydrothermal contribution. These characteristics support the interpretation that the greenalite formed in a mixing zone near deep-water vents where Fe2+-rich and SiO2(aq)-rich hydrothermal fluids mixed with seawater to produce gradients in pH and temperature amenable to greenalite genesis. The greenalite nanoparticles then accumulated hundreds of kilometers from this formation zone, being found in continental shelf metalliferous ores formed beneath upwelling zones, as indicated by sedimentologic, stratigraphic, and paleogeographic constraints. The presence of these greenalite-bearing Mn and Fe-Mn deposits on distal shelves (below storm wave base) records a profound regional oxygen-stratified water column in South China, characterized by hydrothermal input and anoxic bottom waters. These conditions are reminiscent of Precambrian oceans. Thus, greenalite-bearing Mn deposits provide a new archive to track hydrothermal metal fluxes and anoxia in Phanerozoic basins, with potential implications for other Phanerozoic hydrothermal chert and metal deposits.
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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) | 0.002 | 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 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".