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Comprehensive Multidimensional Chromatography

2015· article· en· W2765251120 on OpenAlexaff
Matthew Edwards, Haleigh Boswell, Tadeusz Górecki

Bibliographic record

VenueCurrent Chromatography · 2015
Typearticle
Languageen
FieldChemistry
TopicAnalytical Chemistry and Chromatography
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsTwo-dimensional gasMultidimensional systemsMultidimensional analysisTwo-dimensional chromatographySeparation (statistics)Multidimensional dataGas chromatographyComputer scienceBiochemical engineeringChromatographyChemistryMathematicsMachine learningEngineeringData mining

Abstract

fetched live from OpenAlex

As analysts enquire further into the complexity of their samples, powerful separation techniques become an important asset. The ability (or lack thereof) of conventional one-dimensional separations such as liquid and gas chromatography to resolve complicated mixtures was described in detail by Giddings and others in the 1980s. At this point the development of multidimensional separation techniques began to accelerate. These techniques can offer up to an order of magnitude greater peak capacity than conventional chromatographic methods. Today, multidimensional separation methods are used widely throughout many fields in both industry and academia. This review focuses on comprehensive multidimensional techniques in gas and liquid chromatography. Fundamental aspects of the techniques, modulation technology and recent applications in fields that frequently use the technology will be discussed. Multidimensional separation techniques are here to stay and will undoubtedly play increasingly important role in the analysis of complex chemical samples for years to come. Keywords: Comprehensive multidimensional separations, comprehensive two-dimensional gas chromatography, comprehensive two-dimensional liquid chromatography, environmental, flavours and fragrances, instrumentation.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.651
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.041
GPT teacher head0.286
Teacher spread0.244 · 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.

Study designNot applicable
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

Citations24
Published2015
Admission routes1
Has abstractyes

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