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Record W2038367257 · doi:10.1021/ef0340099

Analysis of Petroleum Resins Using Electrospray Ionization Tandem Mass Spectrometry

2004· article· en· W2038367257 on OpenAlexaff
Darren J. Porter, P. Mayer, Mervin F. Fingas

Bibliographic record

VenueEnergy & Fuels · 2004
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsEnvironment and Climate Change CanadaUniversity of Ottawa
Fundersnot available
KeywordsDiesel fuelChemistryMass spectrometryElectrospray ionizationMass spectrumPetroleumTandem mass spectrometryElectrosprayPetroleum productChromatographyAnalytical Chemistry (journal)Environmental chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Electrospray ionization mass spectrometry (ESI−MS) is becoming a common method for the analysis of petroleum-based chemicals. Here, we have demonstrated the usefulness of this method for the analysis of polar resin fractions of crude oil, fuel oil, and diesel. The mass spectra of crude oil resins can be used to make comparisons between the constituents present in these oils. Tandem mass spectra of one crude oil sample identified a major constituent to be alkylated carbazoles. The analysis of resins from a diesel sample showed the presence of quinoline compounds, along with oxygen or sulfur heterocycles and alkyl carbazoles. The effect of weathering on the composition of petroleum resins was studied by comparing the average number molecular weights generated from mass spectra.

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 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 score0.113
Threshold uncertainty score0.879

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.009
GPT teacher head0.237
Teacher spread0.228 · 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.

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

Citations34
Published2004
Admission routes1
Has abstractyes

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