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Record W3210620395 · doi:10.1039/d1ee02395k

Harnessing the power of machine learning for carbon capture, utilisation, and storage (CCUS) – a state-of-the-art review

2021· review· en· W3210620395 on OpenAlexafffund
Yongliang Yan, Tohid N. Borhani, Sai Gokul Subraveti, Kasturi Nagesh Pai, Vinay Prasad, Arvind Rajendran, Paula Nkulikiyinka, Jude O. Asibor, Zhien Zhang, Ding Shao, Lijuan Wang, Wenbiao Zhang, Yong Yan, William Ampomah, Junyu You, Meihong Wang, Edward J. Anthony, Vasilije Manović, Peter T. Clough

Bibliographic record

VenueEnergy & Environmental Science · 2021
Typereview
Languageen
FieldEngineering
TopicCarbon Dioxide Capture Technologies
Canadian institutionsUniversity of Alberta
FundersPetroleum Technology Development FundEngineering and Physical Sciences Research CouncilNational Energy Technology LaboratoryNational Natural Science Foundation of ChinaCanada First Research Excellence FundCranfield UniversityUniversity of AlbertaKwame Nkrumah University of Science and TechnologyU.S. Department of Energy
KeywordsState (computer science)Power (physics)Computer scienceCarbon fibersState of artProcess engineeringData scienceEngineeringPhysicsProgramming language

Abstract

fetched live from OpenAlex

A review of the state-of-the-art applications of machine learning for CO 2 capture, transport, storage, and utilisation.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.012
GPT teacher head0.226
Teacher spread0.215 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations293
Published2021
Admission routes2
Has abstractyes

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