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Record W1995312757 · doi:10.1002/cjce.5450820307

Development of Two‐point Dynamic Method for Evaluating Extraction Columns

2004· article· en· W1995312757 on OpenAlexvenueno aff
Hongbo Li, Xiaojin Tang, Guangsheng Luo, J. Wang

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2004
Typearticle
Languageen
FieldEngineering
TopicHeat and Mass Transfer in Porous Media
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsSieve (category theory)Mass transferExtraction (chemistry)Mixing (physics)Point (geometry)Position (finance)Column (typography)MechanicsDiffusionMaterials scienceComputer scienceChromatographyMathematicsChemistryGeometryPhysicsThermodynamics

Abstract

fetched live from OpenAlex

Abstract The steady‐state method by measuring the concentration profile along the column height is an effective way, but it is a time and material consumption method for large extraction columns. In order to investigate the axial‐mixing and mass transfer performances in a large pulsed‐sieve‐plate extraction column with the diameter of 150mm, a two‐point dynamic method with mass transfer based on the diffusion model has been developed. The results proved that the two‐point dynamic method has the advantages of good accuracy, simple boundary equations and flexible sampling position over the traditional single‐point dynamic method. It is a reliable tool for studying the axial‐mixing and the mass transfer performances.

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 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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.265
Threshold uncertainty score0.392

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 head0.019
GPT teacher head0.286
Teacher spread0.267 · 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 teacher head, 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

Citations5
Published2004
Admission routes1
Has abstractyes

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