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Determination of the Settling Rate of Aggregates Using the Ultrasound Method during Paraffinic Froth Treatment

2016· article· en· W2512332454 on OpenAlexafffund
Dominik Kosior, Edwina Ngo, Tadeusz Dąbroś

Bibliographic record

VenueEnergy & Fuels · 2016
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsNatural Resources Canada
FundersGovernment of Canada
KeywordsSettlingIsopentaneUltrasoundAsphaltMaterials scienceSettling timeChemistryAnalytical Chemistry (journal)ChromatographyMineralogyEnvironmental scienceComposite materialOrganic chemistryEnvironmental engineeringPhysicsAcoustics

Abstract

fetched live from OpenAlex

This paper presents results of a study on the settling rate of aggregates formed during paraffinic treatment of bitumen froth. Experiments were performed at temperatures ranging from 30 to 90 °C and various solvent/bitumen ratios, using n -pentane, isopentane, and n -hexane as paraffinic solvents. Characteristic settling curves of diluted bitumen froth were obtained by two independent methods: visual observation and an ultrasound technique. The ultrasound study showed that the hindered settling zone interface can be accurately tracked in place using an ultrasound velocity profiler. Problems associated with determining the settling rate by fitting a linear function to the initial part of the settling curve of the upper interface at higher temperatures were avoided by applying the so-called lower interface method. Settling rates determined using the upper and lower interface methods showed good agreement, confirming interchangeability of the methods.

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)0.001
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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.016
GPT teacher head0.268
Teacher spread0.253 · 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

Citations8
Published2016
Admission routes2
Has abstractyes

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