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Record W4362665002 · doi:10.3390/min13040515

Catalysis of Minerals in Pyrolysis Experiments

2023· article· en· W4362665002 on OpenAlexaff
Ming Zhong, Haiping Huang, Pengcheng Xu, Jie Hu

Bibliographic record

VenueMinerals · 2023
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsUniversity of Calgary
FundersNational Natural Science Foundation of China
KeywordsMineralUraniumCatalysisPyrolysisClay mineralsCarbonate mineralsGeochemistryCarbonateMineralogyAlkali metalGeologyChemistryMaterials scienceMetallurgyOrganic chemistry

Abstract

fetched live from OpenAlex

Recent research in the field of oil and gas geochemistry has focused on the catalytic role of minerals in geological history. Thermal simulation experiments are considered a valuable means of studying the formation and transformation of hydrocarbons. In this paper, we review the catalytic mechanisms, processes, and various arguments for different types of minerals in thermal simulation experiments from the perspective of mineral additives. We focus on two categories: (1) minerals that provide direct catalysis, such as clay minerals, alkali metals, carbonate rocks, and some transition metal elements, and (2) minerals, such as serpentine, that promote aqueous hydrogen and act as the material basis, as well as the radioactive element uranium. We also discuss existing disputes and prospects for the development direction of mineral catalytic thermal simulation experiments.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.203
Threshold uncertainty score0.472

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.024
GPT teacher head0.264
Teacher spread0.240 · 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 designBench or experimental
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

Citations16
Published2023
Admission routes1
Has abstractyes

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