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Record W1984476818 · doi:10.1002/aic.11639

Impact of sampling method and scale on the measurement of mixing and the coefficient of variance

2008· article· en· W1984476818 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

VenueAIChE Journal · 2008
Typearticle
Languageen
FieldEngineering
TopicWater Systems and Optimization
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMixing (physics)StriationMicromixerTransectLaminar flowTurbulenceSampling (signal processing)Dispersion (optics)Scale (ratio)StatisticsReynolds numberMechanicsMathematicsMaterials scienceOpticsPhysicsGeology

Abstract

fetched live from OpenAlex

Abstract Spatial statistics methods are used to determine the effect of the sampling scale and method on two measures of mixing: the coefficient of variance CoV and the maximum striation thickness. Three sampling methods: quadrats, probes and transects, were tested. Two CFD data sets were used as test cases: dispersion of floating particles in a turbulent stirred tank and laminar mixing of tracer particles in a micromixer. Over 100 probes are needed to track the evolution of the CoV, and the probe size should match the smallest mixing scale of interest. The final value of the CoV varies by up to a factor of 5 as the probe size increases. The most useful measurement is the one which changes the most in the later stages of mixing: intensity of segregation, or CoV, for the turbulent case; and scale of segregation, or maximum striation thickness on a transect, for the laminar case. © 2008 American Institute of Chemical Engineers AIChE J, 2008

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.336
Threshold uncertainty score0.093

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.037
GPT teacher head0.254
Teacher spread0.218 · 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