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Record W4307639457 · doi:10.1144/sp528-2022-160

An overview of underground energy-related product storage and sequestration

2022· article· en· W4307639457 on OpenAlexaff
Richard A. Schultz, Niklas Heinemann, Birgit Horváth, John Wickens, Johannes Miocic, Oladipupo Babarinde, Wenzhuo Cao, Paolo Capuano, Thomas Dewers, Maurice B. Dusseault, Katriona Edlmann, Raven A. Goswick, Aliakbar Hassanpouryouzband, Taha Husain, Wencheng Jin, Jingyao Meng, Seunghee Kim, Fatemeh Molaei, Tosin Odunlami, Umesh Prasad, Qinghua Lei, Brandon Schwartz, J. M. Segura, Hamed Soroush, S. Voegeli, Sherilyn Williams‐Stroud, Haitao Yu, Qi Zhao

Bibliographic record

VenueGeological Society London Special Publications · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicCO2 Sequestration and Geologic Interactions
Canadian institutionsUniversity of WaterlooStantec (Canada)Alberta Energy
Fundersnot available
KeywordsContainment (computer programming)SustainabilityEnvironmental economicsResilience (materials science)Carbon sequestrationSupply chainEnergy securityEnvironmental resource managementBusinessCorporate governanceGreenhouse gasEnvironmental scienceEnergy supplyNatural resource economicsEnergy (signal processing)EngineeringComputer scienceRenewable energy

Abstract

fetched live from OpenAlex

Abstract Storage of energy-related products in the geological subsurface provides reserve capacity, resilience, and security to the energy supply chain. Sequestration of energy-related products ensures long-term isolation from the environment and, for CO 2 , a reduction in atmospheric emissions. Both porous-rock media and engineered caverns can provide the large storage volumes needed for energy security and supply-chain resilience today and in the future. Methods for site characterization and modelling, monitoring, and inventory verification have been developed and deployed to identify and mitigate geological threats and hazards such as induced seismicity and loss of containment. Broader considerations such as life-cycle analysis, environment, social and governance (ESG) impact and effective engagement with stakeholders can reduce project uncertainty and cost while promoting sustainability during the ongoing energy transition toward net-zero or low-carbon economies.

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 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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.790
Threshold uncertainty score0.976

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0250.000

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 head0.039
GPT teacher head0.288
Teacher spread0.249 · 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 teacher head, not a consensus.

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

Citations21
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueGeological Society London Special PublicationsSame topicCO2 Sequestration and Geologic InteractionsFrench-language works237,207