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Record W3007167774 · doi:10.1306/eg.10221919015

An overview of carbon capture and storage atlases around the world

2020· article· en· W3007167774 on OpenAlexaboutno aff
Mariana Ciotta, Drielli Peyerl, Ligia Vizzeu Barrozo, Lucy Sant Anna, Edmílson Moutinho dos Santos, Célio Bermann, Carlos Henrique Grohmann, Evandro Mateus Moretto, Colombo Celso Gaeta Tassinari

Bibliographic record

VenueEnvironmental Geosciences · 2020
Typearticle
Languageen
FieldEngineering
TopicCarbon Dioxide Capture Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsCarbon capture and storage (timeline)Carbon fibersComputer scienceGeologyEnvironmental scienceRemote sensingSystems engineeringEngineeringOceanographyAlgorithmClimate change

Abstract

fetched live from OpenAlex

ABSTRACT Recent concerns about climate change and greenhouse gas emissions have a clear effect on the energy sector, directly affecting the use of fossil fuels. Companies and countries that depend on these sources of energy (so-called not clean) take actions to search for palliative solutions. The production of atlases of carbon capture and storage (CCS) is one of the collaborative actions that seeks to systematize and organize several aspects involving the use of CCS technologies. This paper focuses on an analytical overview of approaches addressed by five different CCS atlases published by Brazil, the United States, Canada, Mexico, Norway, and South Africa. The five atlases are available for public access; an analytical overview could substantiate the academic and technical decisions related to the future publication of a new atlas for any country and suggests the inclusion of new topics such as social and environmental issues.

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)0.003
metaresearch head score (Gemma)0.005
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: none
Teacher disagreement score0.032
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0320.055
Science and technology studies0.0010.001
Scholarly communication0.0030.005
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.002

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 head0.020
GPT teacher head0.220
Teacher spread0.200 · 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

Citations12
Published2020
Admission routes1
Has abstractyes

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