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

2002· article· en· W2142540792 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

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.

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)0.001
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 score0.060
Threshold uncertainty score0.401

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.006
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.084
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