Iron Speciation of Mud Breccia from the Dushanzi Mud Volcano in the Xinjiang Uygur Autonomous Region, NW China
Bibliographic record
Abstract
Organic-inorganic interactions occurring in petroleum-related mud volcanoes can help predict the chemical processes that are responsible for methane emissions to the atmosphere. Seven samples of mud breccia directly ejected from one crater were collected in the Dushanzi mud volcano, along with one argillite sample of the original reddish host rocks distal from the crater, for comparison purposes. The mineral and chemical compositions as well as iron species of all samples were determined using XRD, XRF and Mossbauer spectroscopy, respectively. The results indicate that a series of marked reactions occurred in the mud volcano systems, more specifically in the mud breccia when compared to the original rocks. Changes mainly included: (1) some conversion of clay minerals from smectite into chlorite and illite, and the precipitation of secondary carbonate minerals such as calcite and siderite; (2) silicon depletion and significant elemental enrichment of iron, manganese, magnesium, calcium and phosphorus; and (3) transformation of iron from ferric species in hematite and smectite into ferrous species in siderite, chlorite and illite. These geochemical reactions likely induced the color changes of the original reddish Neogene argillite to the gray or black mud breccia, as a result of reduction of elements and/or alteration of minerals associated with the oxidation of hydrocarbons. Our results also suggest that greenhouse gases emitted from the mud volcanoes are lowered through a series of methane oxidation reactions and carbon fixation (i.e., through carbonate precipitation).
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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".