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Assessing the Depositional Environment of Cretaceous Ge-Rich Coals in the Wulantuga Mine, Shengli Coalfield, Northeastern China

2024· article· en· W4391217995 on OpenAlexaff
Xiaoshuai Wang, Yifan Chen, Yuegang Tang, Rajender Gupta, Harold H. Schobert, James C. Hower, Longyi Shao, Tengda Ma

Bibliographic record

VenueEnergy & Fuels · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCoal and Its By-products
Canadian institutionsUniversity of Alberta
FundersChina Scholarship CouncilNational Natural Science Foundation of China
KeywordsSedimentary depositional environmentSwampMaceralPeatCoalMireFaciesGeologyCoal miningGeochemistryEcosystemMarshEcological successionCretaceousMining engineeringWetlandPaleontologyPetrographyChemistryEcologyGeographyArchaeology

Abstract

fetched live from OpenAlex

To provide a new perspective on the formation of the Ge-rich coals, the depositional environment of the Wulantuga coals was studied with the incorporation of coal maceral and geochemistry-based indicators. The results show that the No.6 coal seam in the Wulantuga mine was formed in a mire with a succession of swamps, fens, and marsh. The average contents of Ge in coals formed in different mires, from high to low, are swamp (220 μg/g), marsh (205 μg/g), and fen (185 μg/g). The accumulation of the No.6 seam has been divided into four stages from bottom to top based on the identified coal facies types. The reducing condition and gelification of the ecosystem environment ranged from strong to weak, to strong, and back to weak. The variation of Ge concentrations also occurs in the same way. Strong reduction and gelification of the ecosystem environments can favor Ge enrichments in the Wulantuga coals. Sufficient sources and favorable conditions are essential for the unusual Ge enrichments in coals. This study provides a new perspective for the depositional environment of Ge-rich coals and is useful for the exploration of Ge-rich coal resources.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.446
Threshold uncertainty score0.730

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)0.0010.000

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 head0.014
GPT teacher head0.217
Teacher spread0.203 · 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 designObservational
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

Citations2
Published2024
Admission routes1
Has abstractyes

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