MétaCan
Menu
Back to cohort
Record W2116339295 · doi:10.1093/chromsci/44.2.101

Gas Chromatographic Applications with the Dielectric Barrier Discharge Detector

2006· article· en· W2116339295 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Chromatographic Science · 2006
Typearticle
Languageen
FieldMedicine
TopicPlasma Applications and Diagnostics
Canadian institutionsDow Chemical (Canada)
FundersUniversity of HoustonDow Chemical Company
KeywordsDetectorChemistryGas chromatographySelectivityChromatographyStationary phaseSemiconductor detectorCurrent (fluid)Analytical Chemistry (journal)SemiconductorGas phaseOptoelectronicsCatalysisOrganic chemistryMaterials scienceOpticsElectrical engineering

Abstract

fetched live from OpenAlex

With gas chromatography, there are many more choices for detectors when compared to other separation disciplines in analytical chemistry. The presence of sensitive and selective detectors aids in easing the separation requirements imposed on the capillary column. The current gas phase detectors, however, do not completely fulfill contemporary analytical needs. One example is in the area of ultratrace analysis of permanent gases for semiconductor industry. Another example is in the area of environmental/industrial hygiene monitoring for compounds such as 1,3-butadiene or vinyl chloride. The dielectric barrier discharge detector, a new highly sensitive detector with tuneable selectivity, has recently been innovated and commercialized. In this paper, the principle of operation of the detector, along with critical challenging industrial applications such as the analysis of oxygenated compounds, sulfur-containing compounds, and other compounds of industrial significance is presented.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.606
Threshold uncertainty score0.459

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.005
Science and technology studies0.0010.001
Scholarly communication0.0000.000
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 head0.005
GPT teacher head0.231
Teacher spread0.226 · 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