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Record W2324614750 · doi:10.2113/gselements.9.2.115

Serpentinite Carbonation for CO2 Sequestration

2013· article· en· W2324614750 on OpenAlexafffund
Ian Power, Sasha Wilson, Gregory M. Dipple

Bibliographic record

VenueElements · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicCO2 Sequestration and Geologic Interactions
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaCarbon Management Canada
KeywordsCarbonationCarbon sequestrationPulmonary sequestrationEnvironmental scienceGeologyNatural resource economicsChemistryCarbon dioxideChemical engineeringEconomicsEngineeringPhilosophy

Abstract

fetched live from OpenAlex

Abstract Serpentinites offer a highly reactive feedstock for carbonation reactions and the capacity to sequester carbon dioxide (CO2) on a global scale. CO2 can be sequestered in mined serpentinite using high-temperature carbonation reactors, by carbonating alkaline mine wastes, or by subsurface reaction through CO2 injection into serpentinite-hosted aquifers and serpentinized peridotites. Natural analogues to serpentinite carbonation, such as exhumed hydrothermal systems, alkaline travertines, and hydromagnesite–magnesite playas, provide insights into geochemical controls on carbonation rates that can guide industrial CO2 sequestration. The upscaling of existing technologies that accelerate serpentinite carbonation may prove sufficient for offsetting local industrial emissions, but global-scale implementation will require considerable incentives and further research and development.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.001

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 head0.015
GPT teacher head0.265
Teacher spread0.251 · 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 designBench or experimental
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

Citations178
Published2013
Admission routes2
Has abstractyes

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