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Record W4392376677 · doi:10.1002/jqs.3612

Mechanism of organic matter enrichment in a basin with shallow biogenic gas: a case study of Pleistocene shale in the Qaidam Basin

2024· article· en· W4392376677 on OpenAlexaff
Xiaoxue Liu, Zhenxue Jiang, Xianglu Tang, Zeyu Shao, Mingshuai Xu

Bibliographic record

VenueJournal of Quaternary Science · 2024
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsSimon Fraser University
FundersChina Scholarship CouncilNational Natural Science Foundation of China
KeywordsGeologyStructural basinOil shalePleistoceneShale gasGeochemistryOrganic matterPaleontologyGeomorphologyChemistry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.664
Threshold uncertainty score0.265

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.246
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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