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Record W4315780269 · doi:10.1039/d2va00236a

Prospects of carbon capture, utilization and storage for mitigating climate change

2023· article· en· W4315780269 on OpenAlexafffund
Poritosh Roy, Amar K. Mohanty, Manjusri Misra

Bibliographic record

VenueEnvironmental Science Advances · 2023
Typearticle
Languageen
FieldEngineering
TopicCarbon Dioxide Capture Technologies
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of CanadaOntario Ministry of Economic Development, Job Creation and TradeMinistero dello Sviluppo EconomicoAgriculture and Agri-Food CanadaMinistry of Agriculture, Food and Rural AffairsOntario Ministry of Economic Development and InnovationOntario Ministry of Agriculture, Food and Rural Affairs
KeywordsClimate changeCarbon capture and storage (timeline)Scale (ratio)Environmental scienceEnvironmental resource managementClimate change mitigationEnvironmental economicsRisk analysis (engineering)Environmental planningBusinessGeographyEconomicsEcology

Abstract

fetched live from OpenAlex

CCUS have gained attention to combat rising climate change challenges. Transdisciplinary approaches are required for deploying large-scale CCUS projects. Acceptance of CCU seems higher than CCS while CCS is cost-effective compared to CCU.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0020.004
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0160.002

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 head0.017
GPT teacher head0.238
Teacher spread0.220 · 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 designTheoretical or conceptual
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

Citations94
Published2023
Admission routes2
Has abstractyes

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