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Characterization of fractured serpentinite at Shulaps, Coquihalla, and Tulameen sites in British Columbia: Implications for carbon storage

2025· article· en· W4411449452 on OpenAlexafffundabout
Katrin Steinthorsdottir, Gregory M. Dipple, Xueya Lu, Sandra Ó. Snæbjörnsdóttir, R J Enkin

Bibliographic record

VenueApplied Geochemistry · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicCO2 Sequestration and Geologic Interactions
Canadian institutionsGeological Survey of CanadaNatural Resources CanadaUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of British Columbia
KeywordsGeologyCarbon fibersGeochemistryCharacterization (materials science)MineralogyMaterials scienceNanotechnology

Abstract

fetched live from OpenAlex

: This study presents a geological characterization of variably serpentinized harzburgite and dunite of the Shulaps, Coquihalla, and Tulameen sites in southwestern British Columbia. Surface geological data are used to determine the injectivity and reactivity for carbon storage via shallow CO 2 injection and mineralization. Injectivity into a fracture-hosted reservoir was assessed by measuring fracture intensity and connectivity, which were quantified on an outcrop scale and considered together with the physical properties of rock samples collected from the field sites. Pervasively serpentinized harzburgite from Shulaps and Coquihalla are more fractured than partially serpentinized harzburgite and dunite. Additionally, rock mineral content, bulk rock chemical composition and dissolution rates of selected samples were used to assess reactivity. Serpentinized dunite from Tulameen is more reactive than serpentinized harzburgite. The data collected can help assess baseline data, modeling inputs, and well-targeting.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.117
Threshold uncertainty score0.236

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0030.001
Scholarly communication0.0020.000
Open science0.0010.001
Research integrity0.0010.000
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 head0.005
GPT teacher head0.218
Teacher spread0.213 · 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

Citations0
Published2025
Admission routes3
Has abstractno

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