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Record W4241487168 · doi:10.1515/iupac.88.0317

Electromembrane Extraction (EME)

2017· dataset· pt· W4241487168 on OpenAlexaff
Colin F. Poole, Zoltán Mester, Manuel Miró, Stig Pedersen‐Bjergaard, Janusz Pawliszyn

Bibliographic record

VenueIUPAC Standards Online · 2017
Typedataset
Languagept
FieldChemistry
TopicAnalytical chemistry methods development
Canadian institutionsUniversity of WaterlooNational Research Council Canada
Fundersnot available
KeywordsExtraction (chemistry)Computer scienceProcess engineeringSample (material)Scale (ratio)Sample preparationThroughputChromatographyBiochemical engineeringChemistryEngineeringPhysics

Abstract

fetched live from OpenAlex

Approaches for analytical-scale extraction are developing rapidly as new strategies are implemented to improve sample throughput, to minimize material use in laboratory methods, and to develop on-site capabilities. In this contribution, definitions and recommendations for symbols for the terms used in analytical extraction are presented. Exhaustive, microextraction, elevated temperature, microwave- and ultrasound-assisted, parallel batch, flow through systems, and membrane extraction approaches are discussed. An associated tutorial titled “Extraction” provides a detailed introduction to the topic.

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)0.002
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.049
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0490.000

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 head0.028
GPT teacher head0.461
Teacher spread0.433 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreDataset

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
Published2017
Admission routes1
Has abstractyes

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