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Record W2086357630 · doi:10.1080/02652030902759046

Aniline in vegetable and fruit samples from the Canadian total diet study

2009· article· en· W2086357630 on OpenAlexafffundabout
Xu‐Liang Cao, Jiping Zhu, Stephen MacDonald, Kaela Lalonde, Bob Dabeka, Mamady Cissé

Bibliographic record

VenueFood Additives & Contaminants Part A · 2009
Typearticle
Languageen
FieldChemistry
TopicAnalytical chemistry methods development
Canadian institutionsEnvironment and Climate Change CanadaHealth Canada
FundersHealth CanadaU.S. Environmental Protection Agency
KeywordsAnilineIsotope dilutionChemistryRepeatabilityPesticideDetection limitToxicologyChromatographyBiologyMass spectrometryAgronomy

Abstract

fetched live from OpenAlex

An isotope dilution method based on solvent extraction followed by GC-MS analysis was developed and used to determine aniline in vegetable and fruit samples collected from the Canadian total diet study. Aniline was not detected in any of the 23 vegetable samples from the 2005 total diet study at a method detection limit of 0.01 mg kg(-1). Among the 16 fruit samples, it was detected only in apple samples, with an average concentration of 0.278 mg kg(-1). Aniline was not detected in apple samples collected in the 2002, 2003, 2006 or 2007 total diet studies, but it was detected in the apple samples collected from the 2001 and 2004 studies, at concentrations of 0.085 and 0.468 mg kg(-1), respectively. The average aniline concentration for the 2001, 2004 and 2005 apple samples was 0.277 mg kg(-1). Good repeatability of the method was observed with replicate analysis of apple samples, with relative standard deviations (RSD) ranging 3.8-21% and an average of 11%.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.216
Threshold uncertainty score0.434

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.035
GPT teacher head0.280
Teacher spread0.245 · 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 designObservational
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

Citations3
Published2009
Admission routes3
Has abstractyes

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