Hydrocarbon Generation Potential and Molecular Composition of Eocene Guchengzi Formation Coals and Carbonaceous Mudstones from the Fushun Basin, NE China
Bibliographic record
Abstract
A coal seam from the Fushun Basin in NE China was investigated pertaining to its bulk and molecular compositions to elucidate its hydrocarbon generation potential. Eocene Guchengzi Formation coals and carbonaceous mudstones were deposited in oxic environments and fell within the subbituminous A rank. Hydrogen index (HI) values spanned within a range from 139 to 495 mg HC/g total organic carbon (TOC), indicating the presence of Type II2-III kerogens. The pyrolytic hydrocarbon yield (S2) linearly increased with TOC content in carbonaceous mudstones, while there was no evident correlation between S2 and TOC in the coals. Molecular compositions revealed that the input of algae and aquatic biomass did not enhance the hydrocarbon generation potential of the coals. Moreover, the hydrocarbon generation potential of the coals was not influenced by changes in flora. However, the degree of terpenoid aromatization showed a clear correlation with the HI values. A higher hydrocarbon generation potential is associated with a lower degree of aromatization, even though the Pristane/Phytane ratio does not indicate such a correlation. Bacterial transformation played a pivotal role in the structural rearrangement of the coal matrix, resulting in hydrogen incorporation into the coal. This ultimately led to a relatively hydrogen-rich composition with high oil-generating potential.
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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 | 0.000 | 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 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".