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Record W2085598193 · doi:10.4314/swj.v4i3.51848

The future of Carbon Capture and Storage (CCS) in Nigeria

2010· article· en· W2085598193 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueScience World Journal · 2010
Typearticle
Languageen
FieldEnergy
TopicOil, Gas, and Environmental Issues
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsCarbon capture and storage (timeline)Greenhouse gasClean Development MechanismWaste managementCarbon fibersEnvironmental economicsEnvironmental scienceBusinessNatural resource economicsEnvironmental protectionEngineeringComputer scienceClimate changeEconomics

Abstract

fetched live from OpenAlex

Carbon Capture and Storage (CCS) is one of the techniques for greenhouse gas (GHG) emissions reduction. This article reviews the current status of CCS technology, highlights costs and discusses legal and regulatory issues of CCS. The main purpose of the article is to review CCS and CO2-EOR experience from ongoing projects in different parts of the world and give recommendations on how this knowledge can be applied in Nigeria. A potential demonstration CO2-EOR project in Nigeria under the Clean Development Mechanism (CDM) is discussed. Keywords: Carbon Capture and Storage; Clean Development Mechanism; CO2-Enhanced Oil Recovery; Nigeria

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.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.626
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.004
GPT teacher head0.217
Teacher spread0.213 · 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