MétaCan
Menu
Back to cohort
Record W1973017308 · doi:10.1515/jnetdy.2007.016

Evaluation of Thermal Diffusion Models for Ternary Hydrocarbon Mixtures

2007· article· en· W1973017308 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Non-Equilibrium Thermodynamics · 2007
Typearticle
Languageen
FieldEngineering
TopicField-Flow Fractionation Techniques
Canadian institutionsUniversity of TorontoToronto Metropolitan University
FundersCanadian Space Agency
KeywordsTernary operationDiffusionThermodynamicsHydrocarbonHydrocarbon mixturesBinary numberThermalThermal diffusivityEquation of stateMaterials scienceChemistryMathematicsComputer sciencePhysicsOrganic chemistry

Abstract

fetched live from OpenAlex

An accurate thermodiffusion model is of paramount importance to the petroleum industry for the prediction of the compositional variation in hydrocarbon reservoirs. As the most recent theoretical development, Kempers and Firoozabadi models can be used for both binary and multicomponent mixtures. In this paper, we verified these models with three ternary hydrocarbon mixtures. The results reveal that the accuracy of the thermal diffusion coefficients relies on the accuracy of the thermodynamic properties from equations of state, corresponding Fick's diffusion coefficients, and the thermal diffusion modeling.

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)0.002
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.650
Threshold uncertainty score0.603

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.016
GPT teacher head0.270
Teacher spread0.254 · 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