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Record W7162283277 · doi:10.3997/2214-4609.2025101071

Geological Carbon Storage in Atlantic Canada Sedimentary Basins: Quantitative Screening, Play Elements and Risk Analysis

2025· article· W7162283277 on OpenAlexaffabout
F. Richards, F.K. Keppie, N. MacAdam, H. Cen, M. Dusseault, G. Wach

Bibliographic record

Venuenot available
Typearticle
Language
FieldEnvironmental Science
TopicCO2 Sequestration and Geologic Interactions
Canadian institutionsUniversity of WaterlooDalhousie University
Fundersnot available
KeywordsSedimentary rockCarbon fibersRisk assessmentStatistical analysisQuantitative analysis (chemistry)

Abstract

fetched live from OpenAlex

Summary We present the first comprehensive quantitiative assessment and risking of geological carbon storage resources onshore and offshore Atlantic Canada (the provinces of Nova Scotia, Newfoundland and Labrador, Prince Edward Island and New Brunswick). On the continental margin, that extends 3800 km from the NE USA to northern Labrador, we build on 3 previous studies and assess storage in 54 hydrocarbon fields and deep saline aquifer systems in 12 Mesozoic-Cenozoic, rift-passive margin basins. We classify these resources under the SPE Storage Resources Management System. In the Gulf of St. Lawrence region we evaluate storage in Paleozic and Triassic sedimentary basins and risk these relative to Mesozic and Cenozoic aquifers in the Atlantic basins by aggregating Chance of Success in four play elements (storage, injectivity, containment and pressure space) and consolidating the results in a “traffic light” risk-adequacy matrix. There are no Atlantic Canada storage resources in the OGCI Carbon Storage Resource Catalogue, and investment is currently hampered by lack of offshore regulations and commercial terms.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.129
Threshold uncertainty score0.260

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.004
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.263
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 designSimulation or modeling
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

Citations0
Published2025
Admission routes2
Has abstractyes

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