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Record W2898726656 · doi:10.3390/geosciences8110397

CCS Risk Assessment: Groundwater Contamination Caused by CO2

2018· article· en· W2898726656 on OpenAlexaffabout
Zhenze Li, Mamadou Fall, Alireza Ghirian

Bibliographic record

VenueGeosciences · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicCO2 Sequestration and Geologic Interactions
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsEnvironmental scienceGroundwaterEnvironmental remediationContaminationLeakage (economics)Risk assessmentWater qualityRisk analysis (engineering)Water resource managementEnvironmental engineeringEngineeringComputer scienceBusinessGeotechnical engineering

Abstract

fetched live from OpenAlex

The potential contamination of underground drinking water (UDW) caused by CO2 leakage is a critical decision input for risk assessment and management decision making. This paper presents an overview of the potential alterations to UDW quality caused by CO2 and the relevant quality guidelines on drinking water. Furthermore, a framework and numerical simulator have been developed to (i) predict and assess the potential consequences of CO2 leakage on the quality of UDW; and (ii) assess the efficiency of groundwater remediation methods and scenarios for various UDW leakage conditions and alterations. The simulator was applied to a Canadian CO2 disposal site to assess the potential consequences of CO2 leakage on groundwater quality. The information, framework, and numerical tool presented here are useful for successful risk assessments and the management of CO2 capture and sequestration in Canadian geological formations.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.219
Threshold uncertainty score0.436

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.274
Teacher spread0.263 · 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 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

Citations14
Published2018
Admission routes2
Has abstractyes

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