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Record W4206074071 · doi:10.1093/ce/zkab056

CO2-capture research and Clean Energy Technologies Research Institute (CETRI) of University of Regina, Canada: history, current status and future development

2021· article· en· W4206074071 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueClean Energy · 2021
Typearticle
Languageen
FieldEngineering
TopicCarbon Dioxide Capture Technologies
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsClean energyRenewable energyCarbon capture and storage (timeline)Clean technologyCarbon fibersGreenhouse gasEnvironmental researchEnvironmental scienceEnvironmental economicsWaste managementEngineeringPolitical scienceEnvironmental protectionEnvironmental planningClimate changeComputer science

Abstract

fetched live from OpenAlex

Summary Clean Energy Technologies Research Institute (CETRI) was formerly known as the International Test Centre for CO2 Capture in the early 2000s. The original focus of the centre was to help lower the carbon intensity of the current energy sources to low-carbon ones in Canada. Currently, CETRI’s mandates have expanded and now include most of the low-carbon and near-carbon-free clean-energy research activities. Areas of research focus include carbon (CO2) capture, utilization and storage (CCUS), near-zero-emission hydrogen (H2) technologies, and waste-to-renewable fuels and chemicals. CETRI also brings together one of the most dynamic teams of researchers, industry leaders, innovators and educators in the clean and low-carbon energy fields.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.828
Threshold uncertainty score0.957

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.037
GPT teacher head0.236
Teacher spread0.199 · 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