Identifying Organic Matter (OM) Types and Characterizing OM Pores in the Wufeng–Longmaxi Shales
Bibliographic record
Abstract
adsorption experiments were performed to study the properties of pores for samples with different TOC and mineral compositions. The results show the following. (1) Pyrobitumen and kerogen can be distinguished under an optical microscope and SEM; the former can be further divided into pyrobitumen without a fixed shape and pyrobitumen with a certain shape, and the latter contains algal fragments, bacteria-like aggregates, graptolite, and micrinite. The overwhelming number of SEM-visible OM pores are mainly observed in pyrobitumen without a fixed shape, whereas pores in other OM types are complex. A PCAS analysis showed that meso-macropores are developed in pyrobitumen without a fixed shape, whereas pores in algal fragments and bacterial-like aggregates are mainly mesopores. (2) Quartz-rich brittle shale will provide more visible SEM pores compared to clay-rich ductile shale, and carbonates are unfavorable for pore development because they can block the pore as cements. Moreover, the rigid mineral framework, including that constructed by quartz recrystallization and pyrite cementation, and the pore-fluid pressure are favorable for the development of OM pores. (3) Adsorption experiments showed that pyrobitumen makes a great contribution to pore development, including micropores and meso-/macropores. Finally, we propose that the pore parameters (e.g., pore diameter, pore form factor, and deformation) of pyrobitumen without a fixed shape may characterize the enrichment condition of shale gas.
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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) | 0.000 | 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 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".