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Technologies extraction of coalbed methane in the Qinshui basin of China

2018· article· en· W2946269476 on OpenAlexaboutno aff
LU Yan-jun, Han Jinxuan, V. V. Shelepov, Elena Yu. Makarova, Kai Li, Chu Jun

Bibliographic record

VenueMoscow University Bulletin Series 4 Geology · 2018
Typearticle
Languageen
FieldEngineering
TopicCoal Properties and Utilization
Canadian institutionsnot available
Fundersnot available
KeywordsCoalbed methaneStructural basinPetroleum engineeringHydraulic fracturingCoalGeologyEnvironmental scienceMining engineeringCoal miningGeochemistryGeomorphologyEngineeringWaste management

Abstract

fetched live from OpenAlex

Coalbed methane (CBM) is a new clean and unconventional energy that has broad space for development. In Qinshui basin, CBM reserves reach 3,97·1012m3, which is 10,8% of the total resource in China. Compared to the main coal basins of USA, Australia, Canada and Russia, coal seams in Qinshui basin have the characteristics of high metamorphism, high gas content, low porosity, low permeability and low reservoir pressure. Therefore, effective stimulation treatments must be used in Qinshui basin to realize industrial exploitation of CBM. At present, vertical wells with hydraulic fracturing are dominated in Qinshui basin. In addition, injection CO2, electric pulse, multi-stage fracturing and other technologies are also applied to the CBM exploitation.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.669
Threshold uncertainty score0.264

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.007
GPT teacher head0.180
Teacher spread0.173 · 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 designNot applicable
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
Published2018
Admission routes1
Has abstractyes

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