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Record W1978371690 · doi:10.1002/cjce.21860

Characterisation of petrologic end members of oil sands from the athabasca region, Alberta, Canada

2013· article· en· W1978371690 on OpenAlexafffundvenueabout
Marek Osacký, Mirjavad Geramian, M. D. Dyar, E. C. Sklute, M. B. Val'ter, Douglas G. Ivey, Qi Liu, Thomas H. Etsell

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2013
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsUniversity of Alberta
FundersSyncrude
KeywordsDolomiteGeologySideritePyriteClay mineralsMineralogyCalciteFeldsparIlliteLepidocrociteMineralQuartzAlbiteGeochemistryKaoliniteGoethiteMaterials scienceChemistryMetallurgyAdsorption

Abstract

fetched live from OpenAlex

Abstract The aim of this study was to perform mineral and chemical characterisation of the four petrologic end members of Alberta oil sands in order to better understand the mineralogical and geochemical factors affecting bitumen extraction. X‐ray diffraction (XRD) results revealed that the petrologic end members contain a variable amount of quartz, clay minerals, carbonates, K‐feldspar, TiO2minerals and pyrite and the Fe‐containing phases were also observed in Mössbauer spectra. Scanning electron microscopy‐energy dispersive X‐ray (SEM‐EDX) analysis also showed the presence of Fe–Ti oxide minerals. Mössbauer results also indicated the presence of lepidocrocite in the fine fractions in amounts below the detection limit of XRD. Interstratified illite–smectite was found only in clay‐rich petrologic end members. Calcite and dolomite were primarily concentrated in the fine fractions of marine petrologic end members. Conversely, siderite was found mainly in the coarse fraction of estuarine petrologic end members. The relative amount of toluene insoluble organics was higher in the fine fractions of marine petrologic end members.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.156
Threshold uncertainty score0.314

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.002
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.154
Teacher spread0.148 · 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 designObservational
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

Citations21
Published2013
Admission routes4
Has abstractyes

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