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Record W2064067666 · doi:10.1155/2013/815841

Estimating Pore Space Hydrate Saturation Using Dissociation Gas Evolution Measurements: In Relevance to Laboratory Testing of Natural or Artificially Synthesised Hydrate-Bearing Soil Specimens

2013· article· en· W2064067666 on OpenAlexafffund
Anuruddhika G. Jayasinghe, J LH Grozic

Bibliographic record

VenueJournal of Geological Research · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHydrateSaturation (graph theory)Clathrate hydrateCharacterisation of pore space in soilDissociation (chemistry)Natural gasMineralogyComputer scienceSoil scienceThermodynamicsMaterials scienceEnvironmental scienceChemistryGeologyGeotechnical engineeringMathematicsPorosityPhysicsPhysical chemistry

Abstract

fetched live from OpenAlex

Physical properties of gas hydrate-bearing soils are known to be greatly affected by the pore space hydrate saturation. The accuracy of most saturation estimation methods is affected by hydrate growth habit and pore space distribution. We highlight the usefulness of dissociation gas evolution measurement (DGEM) method as a reference laboratory method to calibrate most other methods. The DGEM method is based on the concept of mass balance and properties of volume compatibility between two distinct states of a closed system. The accuracy of the estimation depends on (1) the precision with which the laboratory measurements of temperature, pressure, and volumetric properties are obtained (2) and the ability of theoretical models used in the calculation to closely represent the true nature of the system. We perform an analysis to evaluate the sensitivity of the estimation (1) to various laboratory measurements and (2) to the use of different theoretical models to generate a feel for the appropriateness of various assumptions associated with DGEM. The comprehensive guide to available resources useful in the hydrate saturation estimation also serves as one of the major contributions of the work presented.

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)0.005
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.682
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.107
GPT teacher head0.337
Teacher spread0.230 · 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 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

Citations6
Published2013
Admission routes2
Has abstractyes

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