MétaCan
Menu
Back to cohort

Soret Measurement for Multi-Component Hydrocarbon Mixtures from Space Experiment Conducted Onboard FOTON M3 Unmanned Satellite

2012· article· en· W2053896520 on OpenAlexafffund
M. Ziad Saghir, Seshasai Srinivasan, Stéfan Van Vaerenbergh

Bibliographic record

VenueApplied Mechanics and Materials · 2012
Typearticle
Languageen
FieldEngineering
TopicField-Flow Fractionation Techniques
Canadian institutionsToronto Metropolitan University
FundersCanadian Space AgencyEuropean Space Agency
KeywordsComponent (thermodynamics)SatelliteSpacecraftTernary operationThermophoresisHydrocarbonHydrocarbon mixturesConvectionMaterials scienceThermodynamicsAerospace engineeringThermalComputer sciencePhysicsEngineeringChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

In an unprecedented experimental investigation, a binary, a ternary and a four-component hydrocarbon mixture at different pressure have been studied in a nearly convection free environment to understand the thermodiffusion process. Experimental investigations of the mixtures have been conducted in space onboard the spacecraft FOTON-M3. The experiment objective was to measure the thermodiffusion coefficient for multi-component hydrocarbon mixtures. Then the experimental results have also been used to test a thermodiffusion model that has been calibrated based on the results of previous experimental investigations. Results showed a good agreement with current theoretical results except for the four-component system where discrepancies were found and discussed.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.038
GPT teacher head0.247
Teacher spread0.209 · 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

Citations1
Published2012
Admission routes2
Has abstractyes

Explore more

Same venueApplied Mechanics and MaterialsSame topicField-Flow Fractionation TechniquesFrench-language works237,207