MétaCan
Menu
Back to cohort
Record W2987183335 · doi:10.1504/ijram.2019.103332

Risk assessment and management frameworks for carbon capture and geological storage: a global perspective

2019· article· en· W2987183335 on OpenAlexafffund
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
KeywordsCarbon capture and storage (timeline)Transparency (behavior)Government (linguistics)Climate changeCompendiumRisk managementEnvironmental resource managementBusinessEnvironmental planningRisk assessmentRisk analysis (engineering)Environmental economicsEnvironmental scienceComputer scienceGeographyComputer securityFinanceEconomics

Abstract

fetched live from OpenAlex

Carbon capture and storage (CCS) is included in the list of technological processes that could reduce point source carbon dioxide emissions that contribute to climate change. For geological storage projects, global frameworks for environmental and human health risk assessment (RA) and risk management (RM) have been developed within various regional and national jurisdictions as well as by non-government organisations since the 2005 Intergovernmental Panel on Climate Change Special Report on CCS. This article provides an updated compendium of elaborated RA/RM frameworks in leading jurisdictions for CCS in the regulatory and non-regulatory contexts including online resources. Using a 3- or 4-step RA, there is an emphasis on storage site selection and characterisation; an iterative approach is recommended for RM emphasising monitoring and re-assessment; and other risk-based considerations such as communications and transparency are discussed more frequently in non-government guidance. Comprehensive risk estimation is not yet promoted.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.123
Threshold uncertainty score0.568

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 studies0.0000.000
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 head0.007
GPT teacher head0.322
Teacher spread0.315 · 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

Citations15
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueInternational Journal of Risk Assessment and ManagementSame topicCO2 Sequestration and Geologic InteractionsFrench-language works237,207