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Record W2739365169 · doi:10.1039/c7an00908a

Phenol-selective mass spectrometric analysis of jet fuel

2017· article· en· W2739365169 on OpenAlexafffund
Haoxuan Zhu, Eric Janusson, Jingwei Luo, James Piers, Farhana Islam, G. Bryce McGarvey, Allen G. Oliver, Ori Granot, J. Scott McIndoe

Bibliographic record

VenueThe Analyst · 2017
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsImperial Oil (Canada)University of Victoria
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsJet fuelChemistryPhenolJet (fluid)Mass spectrometryChromatographyOrganic chemistryAerospace engineering

Abstract

fetched live from OpenAlex

Bromobenzyl compounds react selectively with phenols via the Williamson ether synthesis. An imidazolium charge-tagged bromobenzyl compound can be used to reveal phenol impurities in jet fuel by analysis via electrospray ionization mass spectrometry. The complex matrix as revealed by Cold EI GC/MS analysis is reduced to a few simple sets of compounds in the charge-tagged ESI mass spectrum, primarily substituted phenols and thiols. Examination of jet fuels treated by different refinery methods reveals the efficacy of these approaches in removing these contaminants.

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)0.000
metaresearch head score (Gemma)0.000
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.016
GPT teacher head0.270
Teacher spread0.254 · 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
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

Citations7
Published2017
Admission routes2
Has abstractyes

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