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Record W2037753759 · doi:10.1081/ss-200026716

A Concept for the Estimation of HETS for Rare Earth Separations in Extraction Columns

2004· article· en· W2037753759 on OpenAlexaff
Baoqiang Liao, Caihua Wan, J. Wang

Bibliographic record

VenueSeparation Science and Technology · 2004
Typearticle
Languageen
FieldEngineering
TopicExtraction and Separation Processes
Canadian institutionsLakehead University
Fundersnot available
KeywordsExtraction (chemistry)ChemistryColumn (typography)Component (thermodynamics)Phase diagramSeparation (statistics)Phase (matter)Range (aeronautics)Analytical Chemistry (journal)PhysicsChromatographyThermodynamicsGeometryMaterials scienceStatistics

Abstract

fetched live from OpenAlex

An approach is proposed to estimate the height equivalent to a theoretical stage (HETS) for multicomponent rare earth separations in extraction columns. Based on the steady concentration profiles of rare earths in the two phases along the extraction column height and the separation factor, a graphical diagram is constructed to determine the number of theoretical stages. The ratio of the two component concentrations in the organic phase and the ratio of the two component concentrations in the aqueous phase times the separation factor are plotted against the height of the column. Rectangular steps are drawn between these two lines to determine the number of theoretical stages and thus HETS. The average HETS for Nd/Pr separation was in the range of 0.86-1.06m under tested conditions.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0000.001
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.018
GPT teacher head0.335
Teacher spread0.318 · 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 designTheoretical or conceptual
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

Citations2
Published2004
Admission routes1
Has abstractyes

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