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Record W2986174722 · doi:10.1504/ijram.2019.103333

The evolution of regulatory practice for CCS projects in Canada

2019· article· en· W2986174722 on OpenAlexafffundabout
Patricia Larkin, William Leiss, Daniel Krewski

Bibliographic record

VenueInternational Journal of Risk Assessment and Management · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicCO2 Sequestration and Geologic Interactions
Canadian institutionsUniversity of Ottawa
FundersNational Academy of SciencesNatural Sciences and Engineering Research Council of CanadaQueen's UniversityMcGill UniversitySocial Sciences and Humanities Research Council of CanadaCarbon Management CanadaUniversity of Ottawa
KeywordsDocumentationTransparency (behavior)Risk assessmentRisk analysis (engineering)Risk managementCarbon capture and storage (timeline)BusinessEnvironmental resource managementComputer scienceEnvironmental scienceComputer securityFinanceClimate change

Abstract

fetched live from OpenAlex

Carbon capture and storage (CCS) pilot and demonstration projects began in Canada in the 1990s. This review of publicly available documentation considers the regulatory application and approval practice for four large Canadian projects that are either under construction or in operation. Results find that oversight of CCS projects is value chain specific and obtaining documentation can be challenging. However, technical risk assessment supporting approvals is moving forward, with an increasing range of chain component health and environmental risks being assessed using referenced approaches. Monitoring remains the primary risk management approach. Global risk estimation is not completed and unresolved issues about transparency in risk communication could have the potential to negatively impact broad public acceptance of CCS and therefore project viability in the long run.

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 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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.120
Threshold uncertainty score0.924

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.006
GPT teacher head0.282
Teacher spread0.276 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations13
Published2019
Admission routes3
Has abstractyes

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