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Record W2320492022 · doi:10.1021/ef400807n

Investigation of the Variation of the Surface Area of Gas Hydrates during Dissociation by Depressurization in Porous Media

2013· article· en· W2320492022 on OpenAlexaff
Anjani Kumar, Brij Maini, P. R. Bishnoi, Matthew A. Clarke

Bibliographic record

VenueEnergy & Fuels · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsClathrate hydrateHydratePorosityPorous mediumCarbon dioxideMaterials scienceCabin pressurizationDissociation (chemistry)Chemical engineeringPermeability (electromagnetism)ThermodynamicsMineralogyChemistryComposite materialPhysical chemistryMembrane

Abstract

fetched live from OpenAlex

Carbon dioxide gas hydrates were formed and decomposed in a high-pressure cell equipped with a flat glass window that was packed with spherical glass beads. The experimental cell was constructed to be, for all practical purposes, a one-dimensional cell, and spherical glass beads were chosen as the porous medium becaus of their high permeability and easily defined geometry. The experiments were conducted at initial water saturations of 20, 25, 30, 35, and 42% with a pressure of 4 MPa during gas hydrate formation and 1.65 MPa during decomposition. The temperature during the experiments was maintained at 4 °C. To identify the functional form for the time-dependent gas hydrate surface area during decomposition, a sensitivity analysis was conducted with the data from one of the experiments. It was found that, in this experimental apparatus, packed with glass beads, the gas hydrate surface area was most closely approximated with a correlation that assumed the hydrate habit to be grain-coating. Subsequently, the selected correlation for the surface area for gas hydrates was used in modeling the data for the other experiments, and it was found that the modeling results agreed excellently with the experimental data.

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 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 score0.314
Threshold uncertainty score0.642

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.005
GPT teacher head0.164
Teacher spread0.159 · 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.

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

Citations26
Published2013
Admission routes1
Has abstractyes

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