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Record W88606764 · doi:10.1093/jaoac/90.2.479

Determination of Benzene in Soft Drinks and Other Beverages by Isotope Dilution Headspace Gas Chromatography/Mass Spectrometry

2007· article· en· W88606764 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of AOAC International · 2007
Typearticle
Languageen
FieldEngineering
TopicAdvanced Chemical Sensor Technologies
Canadian institutionsHealth Canada
Fundersnot available
KeywordsIsotope dilutionChromatographyChemistryMass spectrometryGas chromatographyGas chromatography–mass spectrometryBenzeneDilutionOrganic chemistryPhysics

Abstract

fetched live from OpenAlex

An automated, simple, and reproducible method was developed for the determination of benzene in soft drinks, based on isotope dilution headspace gas chromatography/mass spectrometry in the selected-ion monitoring mode. The method was used to assess benzene levels in samples of 124 soft drinks and beverages. Benzene was not detected in 60% of the 124 products. The average benzene levels in 6 products exceeded the Canadian maximum acceptable concentration of 5 microg/L for benzene in drinking water, and 2 of the 6 products had benzene levels above the World Health Organization guideline of 10 microg/L. The highest level of benzene, 23 microg/L, was found in a soft drink product specifically marketed to children.

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)0.000
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 score0.089
Threshold uncertainty score0.347

Codex and Gemma teacher scores by category

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

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 head0.234
Teacher spread0.229 · 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