Mechanism of organic matter enrichment in a basin with shallow biogenic gas: a case study of Pleistocene shale in the Qaidam Basin
Bibliographic record
Abstract
ABSTRACT Organic matter is the material basis of shale gas. The Qaidam Basin is a key exploration and development area for shallow biogenic shale gas in China. In this study, we have focused on Quaternary Pleistocene shale in the Qaidam Basin, and the mechanism of organic matter enrichment was investigated in terms of water column stratification and paleoclimate. The results show that the K9–K7 section has greater biological productivity than the K5–K4 section. During the early–middle Pleistocene (K9–K7 deposition period), due to a warm and humid climate, the water column was strongly stratified and herbaceous plants developed, resulting in increased biological productivity. Stronger stratification also led to a reducing environment in the lower layer, which was conducive to the preservation of organic matter from the upper layer. During the late Pleistocene (K5–K4 deposition period), with a dry and hot climate, stratification became weaker and the vegetation evolved into woody plants, reducing biological productivity. Weaker stratification led to destruction of the reducing environment, which was not conducive to enrichment of the sedimentary organic matter. Moreover, the increased temperatures increased the activity of methanogenic bacteria, which consumed a lot of the organic matter.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".