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Record W7098407819

Web site: www.ags.gov.ab.ca Suitability of the Alberta Subsurface for Carbon-Dioxide Sequestration in Geological Media

2000· article· en· W7098407819 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCloud Computing and Remote Desktop Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsCarbon sequestrationCaprockCoalSedimentary rockEnhanced oil recoveryCoal miningSupercritical fluidMethaneFossil fuel
DOInot available

Abstract

fetched live from OpenAlex

Executive Summary Sequestration of anthropogenic CO2 in geological media is a potential solution to the release into the atmosphere of CO2, a greenhouse gas. Basically, there are five ways of sequestering CO2 in geological media: 1) through enhanced oil recovery (EOR), 2) storage in depleted oil and gas reservoirs, 3) replacement of methane by CO2 in deep coal beds (ECBMR), 4) injection into deep saline aquifers, and 5) storage in salt caverns. Criteria in assessing the suitability of a sedimentary basin for CO2 sequestration are 1) tectonism and geology, 2) the flow of formation waters, and 3) the existence of storage media (hydrocarbon reservoirs, coal seams, deep aquifers, and salt structures). Because of CO2 properties, identification of the depths of the 31.1ºC isotherm and the 7.38 MPa isobar is essential in establishing if CO2 could be sequestered as a gas, as a liquid, or in a supercritical state. Alberta’s subsurface is tectonically stable. The geology of the undeformed part of the Alberta Basin underlying most of Alberta is very favourable for CO2 sequestration due to its layer-cake structure and the existence of confined regional-scale aquifers, oil and gas reservoirs in various stages of depletion, uneconomic coal seams, and extensive salt beds. There are six regions in

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.459
Threshold uncertainty score0.771

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.4590.175

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 head0.016
GPT teacher head0.234
Teacher spread0.218 · 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.

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

Citations0
Published2000
Admission routes1
Has abstractyes

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