Factors controlling the formation of primary microbial gas in the upper Quaternary sediments of the Jiangsu–Zhejiang coastal plain, eastern China
Bibliographic record
Abstract
ABSTRACT Coastal areas dominated by major rivers are one of the largest carbon sinks worldwide. However, the factors controlling the generation of primary microbial gas therein are still poorly understood. Here, the geochemical characteristics of natural gas and organic-rich muds (organofacies) from the upper Quaternary delta–shelf–estuary system that links the large Changjiang and the adjacent smaller Qiantang River are investigated to understand the key factors controlling the preservation of organic matter and its gas-generation potential. Muds from the floodplain, salt marshes of the paleoestuary, and distal delta front and prodelta of the paleodelta act as efficient gas-generation organofacies. Organic matter in them consists mainly of terrestrial higher land plants and is now undergoing methanogenesis. These organofacies were deposited during transgression accompanied by an intensified Asian summer monsoon that resulted in a greater delivery of organic matter and a higher preservation efficiency and reactivity of organic carbon compared to the subsequent regressive organofacies. Notably, the factors influencing the gas-generation potential in this system varied in a proximal–distal direction. Energetic physical processes acting during the accumulation of the distal delta-front and prodeltaic muds resulted in a longer oxygen exposure time, repetitive redox oscillations, and replenishment of labile marine organic matter, which accelerated microbial degradation before methanogenesis began. Our findings thus suggest that the most efficient gas-generation organofacies in a large river-dominated delta–shelf–estuary system are those deposited in a proximal environment, especially under conditions of rapid aggradation during times of sea-level rise and increased runoff associated with a warm climate.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".