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Record W2312686453 · doi:10.1021/ef200399a

Magnetic Resonance Imaging of Gas Hydrate Formation in a Bed of Silica Sand Particles

2011· article· en· W2312686453 on OpenAlexafffund
Sharareh Bagherzadeh, Igor Moudrakovski, John A. Ripmeester, Peter Englezos

Bibliographic record

VenueEnergy & Fuels · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsSteacie Institute for Molecular SciencesUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHydrateClathrate hydrateNucleationMethaneDissociation (chemistry)Particle sizeVolume (thermodynamics)PorosityMineralogyParticle (ecology)Thermal decompositionMaterials scienceChemistryGeologyThermodynamicsComposite materialPhysical chemistry

Abstract

fetched live from OpenAlex

The formation of methane hydrate in an unconsolidated bed of silica sand was investigated and spatially resolved by employing the magnetic resonance imaging technique. Different sand particle size ranges (210–297, 125–210, 88–177, and <75 μm) and different initial water saturations (100, 75, 50, and 25%) were used. It was observed that hydrate formation in such porous media is not uniform, and nucleation of hydrate crystals occurs at different times and different positions inside the bed. Also, hydrate formation was found to be faster in a bed with lower water content and smaller particle size. Decomposition of hydrate by thermal stimulation at constant volume showed that the dissociation front moves radially inward starting from the external surface of the hydrate formation vessel.

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 score0.001
Threshold uncertainty score0.003

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.195
Teacher spread0.184 · 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

Citations196
Published2011
Admission routes2
Has abstractyes

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