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GC×GC temperature programming requirements to produce bilinear data for chemometric analysis

2002· article· en· W2142540792 on OpenAlexfundno aff
Kevin Johnson, Bryan J. Prazen, Roy K. Olund, Robert E. Synovec

Bibliographic record

VenueJournal of Separation Science · 2002
Typearticle
Languageen
FieldChemistry
TopicAnalytical Chemistry and Chromatography
Canadian institutionsnot available
FundersPartenariat Canadien Contre Le CancerUniversity of WashingtonPacific Northwest National LaboratoryNational Science Foundation
KeywordsChemistryChromatographyBilinear interpolationGas chromatographyChemometricsExtraction (chemistry)Analytical Chemistry (journal)Column (typography)Computer science

Abstract

fetched live from OpenAlex

A diaphragm valve-based comprehensive two-dimensional gas chromatography (GC×GC) instrument with the two columns under independent temperature control is demonstrated. A fifteen-component mixture of alkanes, alkyl aromatics, ketones, and alcohols was separated using this system in only 45 s. Independent temperature control of the two columns allows for high-speed analysis of complex samples while retaining the bilinear data structure that is necessary to apply many chemometric peak-resolving methods. An important part of high-speed GC×GC is sharp injections onto the second column. In this work, 10-ms peak widths on the second column are demonstrated. A peak capacity per time of 240 peaks/min was readily achieved. This work is aimed at providing a high-speed GC system for the quantitative and qualitative analysis of complex process streams, such as natural products.

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)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: Methods
Teacher disagreement score0.010
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0100.004

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 head0.084
GPT teacher head0.378
Teacher spread0.294 · 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 designBench or experimental
Domainnot available
GenreMethods

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

Citations39
Published2002
Admission routes1
Has abstractyes

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