MétaCan
Menu
Back to cohort
Record W2033069721 · doi:10.1016/j.egypro.2011.02.345

A comparative analysis of risk assessment methodologies for the geologic storage of carbon dioxide

2011· article· en· W2033069721 on OpenAlexaff
José Cóndor, Datchawan Unatrakarn, Malcolm Wilson, K. Asghari

Bibliographic record

VenueEnergy Procedia · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicCO2 Sequestration and Geologic Interactions
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsRisk analysis (engineering)Carbon capture and storage (timeline)Adaptation (eye)Lead (geology)Point (geometry)Computer scienceEnvironmental scienceClimate changeBusinessGeology

Abstract

fetched live from OpenAlex

This paper offers a broad summary of the most common risk assessment methodologies for the geologic storage of carbon dioxide. We believe it is valuable to compare these methodologies, particularly in the areas where they lead to similar conclusions. The objective of this paper is to provide a better understanding of the current similarities and differences of these proposed methodologies. Since CCS was proposed as a mitigation option for reducing anthropogenic CO2 emissions, several attempts have been made to study the potential risks of long-term storage of CO2 in geological formations. Various worldwide projects have tried different industrial methods adapted to GSC. In spite of these efforts, currently there is no standardised method or set of methods for evaluating risk and/or uncertainty for GSC projects. Application or adaptation of advanced industrial quantitative risk assessment methods seems not convenient at this point because of lack of specific data. The development of frameworks and qualitative methods looks the most trustable for current projects.

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)0.025
metaresearch head score (Gemma)0.035
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.025
Threshold uncertainty score0.131

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0250.035
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0090.003
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.089
GPT teacher head0.342
Teacher spread0.252 · 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 designTheoretical or conceptual
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

Citations41
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueEnergy ProcediaSame topicCO2 Sequestration and Geologic InteractionsFrench-language works237,207