Characteristics of black shale in the Upper Ordovician Wufeng and lower Silurian Longmaxi formations in the Sichuan Basin and its periphery, China
Bibliographic record
Abstract
Geochemical and mineralogical analyses, in addition to isothermal adsorption experiments on field samples, are used to characterise the sedimentary environments, reservoirs and adsorbed gas of the Upper Ordovician Wufeng–lower Silurian Longmaxi formations in the Sichuan Basin and its peripheral areas. The sedimentary environment of the Wufeng and the lower part of Longmaxi formations is a deep-water shelf with five different lithologies identified: siliceous shale, black shale, siltstone, biolithite limestone and bentonite. The black shale in the Wufeng and the lower part of Longmaxi formations is 50 m thick, with an average organic carbon content (TOC) of 3.81 wt% and a maturity (Ro) of 1.62%. Quartz comprises 54.94 vol% of the shale and positively correlates with the TOC. Micropores in the black shale include intergranular pores, intragranular pores, organic matter pores and microfractures. Among these pores, spaces between clay sheets and organic molecules represent a favourable storage space for the accumulation and preservation of oil and gas. The Langmuir volume parameter ranges between 1.52 and 3.01 cm3/g, with an average value of 2.33 cm3/g. The presence of organic matter pores and pores between clay sheets in the black shale is the main and controlling factor for accumulated gas.
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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 | 0.003 | 0.002 |
| 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".