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Record W2024268369 · doi:10.1021/ef060341j

Vapor Extraction of Heavy Oil and Bitumen:  A Review

2007· review· en· W2024268369 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEnergy & Fuels · 2007
Typereview
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsPetroleum engineeringAsphaltExtraction (chemistry)Process (computing)Process engineeringContext (archaeology)Environmental scienceWaste managementBiochemical engineeringEngineeringComputer scienceMaterials scienceChemistryGeologyChromatography

Abstract

fetched live from OpenAlex

The vapor extraction of heavy oil and bitumen, or Vapex, has emerged as a very promising recovery process since its invention in 1991. The principal reason is the environmental friendliness of Vapex together with its cost-effective nature vis-à-vis other recovery processes. This paper assimilates and presents the research and technological contributions made toward Vapex. The development and applicability of Vapex is brought up in context of the availability of oil from natural sources, challenges of oil recovery, environmental factors, and cost economics. Significant findings and salient features of several experimental and theoretical studies on Vapex are included. Various factors that influence the operation of Vapex are discussed. Important issues are identified that need further investigations for the continued enhancement of Vapex. It is expected that this paper will serve as a useful reference tool for the engineers and scientists interested in Vapex.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.918
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
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 head0.034
GPT teacher head0.329
Teacher spread0.295 · 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