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Record W2898726656 · doi:10.3390/geosciences8110397

CCS Risk Assessment: Groundwater Contamination Caused by CO2

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

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

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.163
Threshold uncertainty score1.000

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 studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0110.001

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