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Record W3001901636

Survey of aflatoxins in beer sold in Canada Part A Chemistry, analysis, control, exposure & risk assessment

2005· article· en· W3001901636 on OpenAlexaboutno aff
M. Mably, M. Mankotia, P. Cavlovic, James P. Tam, Lailai Wong, Peter Pantazopoulos, P. Calway, Peter Scott

Bibliographic record

VenueFood Additives and Contaminants Part A-chemistry Analysis Control Exposure & Risk Assessment · 2005
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMycotoxins in Agriculture and Food
Canadian institutionsnot available
Fundersnot available
KeywordsAflatoxinMycotoxinDetection limitDerivatizationChromatographyChemistryHigh-performance liquid chromatographyFood science
DOInot available

Abstract

fetched live from OpenAlex

Between March 1998 and March 2002, 304 samples of domestic (Canadian) and imported beers from 36 countries were picked up for the determination of aflatoxins B1, B2, G1 and G2. Twelve samples were positive with aflatoxins greater than the limit of quantitation (LOQ) (aflatoxin B1, 4.4 ng l(-1); aflatoxin B2, 3.4 ng l(-1); aflatoxin G1, 11.2 ng l(-1); and aflatoxin G2, 6.2 ng l(-1)). Five samples from Mexico, two samples from Spain and one from Portugal contained aflatoxin B1. Four samples from India contained aflatoxins B1 and B2. The remaining samples contained less than the LOQ for aflatoxins B1, B2, G1 and G2. The analytical method for this survey was based on that of Scott and Lawrence (Scott PM, Lawrence GA. 1997. Determination of aflatoxins in beer. Journal of AOAC International 80:1229-1234.). Aflatoxins B1, B2, G1 and G2 were determined at parts per trillion (ng l(-1)) levels in beer by immunoaffinity column cleanup followed by derivatization with trifluoroacetic acid and reversed-phase liquid chromatography with fluorescence detection.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.742
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.007
GPT teacher head0.216
Teacher spread0.209 · 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.

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

Citations0
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueFood Additives and Contaminants Part A-chemistry Analysis Control Exposure & Risk AssessmentSame topicMycotoxins in Agriculture and FoodFrench-language works237,207