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Record W2317120901 · doi:10.1021/ef300608w

A Rheological and Chemical Investigation of Canadian Heavy Oils From the McMurray Formation

2012· article· en· W2317120901 on OpenAlexaboutno aff
Kejing Li, Casey R. McAlpin, Babajade A. Akeredolu, Ala Bazyleva, Kent J. Voorhees, Robert J. Evans, Michael Batzle, Matthew W. Liberatore, Andrew M. Herring

Bibliographic record

VenueEnergy & Fuels · 2012
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsChemistryFourier transform infrared spectroscopyAsphalteneViscosityMass spectrometryRheologySpectroscopyInfrared spectroscopyAnalytical Chemistry (journal)Organic chemistryChromatographyChemical engineeringMaterials science

Abstract

fetched live from OpenAlex

The prediction of viscosity in the extraction of heavy and viscous oil resources is essential for the economically viable production of these resources. A rheological and chemical investigation of oils from the McMurray formation produced at different depths was undertaken. Chemical analysis using high-resolution time-of-flight mass spectrometry (TOF MS), Fourier transform infrared spectroscopy (FTIR), and nuclear magnetic resonance spectroscopy (NMR) suggested specific compounds representative of the compound classes observed in these heavy oils: [1] water, [2] sec-hexadecyl naphthalene, [3] 2,2′,5,5′-tetramethyl-1,1′-biphenyl, [4] 1-methylanthracene, and [5] cyclopentylcyclopentane. All three analytical techniques detected the monoaromatic, diaromatic, and triaromatic ring hydrocarbons as being the most abundant species in this heavy oil. Specific molecules with intense FTIR modes near 1600 cm –1 and 1380 cm –1 were not identified, and these may account for unknown species in asphaltene fractions. Correlations between heavy-oil chemistry and its viscosity were built using a partial linear square fit (PLS) regression from vibrational modes in the FTIR spectra, predicting an inverse correlation between water and viscosity.

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 score0.027
Threshold uncertainty score0.975

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 head0.017
GPT teacher head0.206
Teacher spread0.190 · 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

Citations22
Published2012
Admission routes1
Has abstractyes

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