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Record W2195955420 · doi:10.1016/j.ijggc.2015.06.017

Recent progress and new developments in post-combustion carbon-capture technology with amine based solvents

2015· article· en· W2195955420 on OpenAlexaff
Zhiwu Liang, Wichitpan Rongwong, Helei Liu, Kaiyun Fu, Hongxia Gao, Fan Cao, Rui Zhang, Teerawat Sema, Amr Henni, Kazi Z. Sumon, Devjyoti Nath, Don Gelowitz, Wayuta Srisang, Chintana Saiwan, Abdelbaki Benamor, Mohammed J. Al‐Marri, Huancong Shi, Teeradet Supap, Christine W. Chan, Qing Zhou, Mohammad R.M. Abu‐Zahra, Malcolm Wilson, Wilfred Olson, Raphael Idem, Paitoon Tontiwachwuthikul

Bibliographic record

VenueInternational journal of greenhouse gas control · 2015
Typearticle
Languageen
FieldEngineering
TopicCarbon Dioxide Capture Technologies
Canadian institutionsUniversity of Regina
FundersOffice of Technology DevelopmentSpecialized Research Fund for the Doctoral Program of Higher Education of ChinaQatar National Research FundNational Natural Science Foundation of China
KeywordsAmine gas treatingCombustionProcess engineeringEngineeringCarbon capture and storage (timeline)Waste managementChemistryOrganic chemistryEnvironmental engineering

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.000
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.011
GPT teacher head0.220
Teacher spread0.209 · 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

Citations591
Published2015
Admission routes1
Has abstractno

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