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Record W2088781314 · doi:10.1080/19440049.2011.559278

Sampling of cereals and cereal-based foods for the determination of ochratoxin A: an overview

2011· review· en· W2088781314 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.

Bibliographic record

VenueFood Additives & Contaminants Part A · 2011
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicMycotoxins in Agriculture and Food
Canadian institutionsHealth CanadaTreasury Board of Canada Secretariat
Fundersnot available
KeywordsOchratoxin ASampling (signal processing)MycotoxinSample (material)OchratoxinEnvironmental scienceStatisticsBiotechnologyMathematicsComputer scienceBiologyChemistryChromatography

Abstract

fetched live from OpenAlex

The mycotoxin ochratoxin A (OTA) is known to be heterogeneously distributed both intrinsically (from one individual food item to the next) as well as distributionally (throughout a sample of individual food items) in cereals and cereal-based foods. Therefore, proper sampling and sample comminution are special challenges, but are prerequisites for obtaining sound analytical data. This paper outlines the issue of the sampling process for cereals and cereal-based foods, starting with the planning phase, followed by the sampling step itself and the formation of analytical samples. The sampling of whole grain and retail-level cereal-based foods will be discussed. Furthermore, possibilities to reduce sampling variance are presented.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.996
Threshold uncertainty score0.533

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.157
GPT teacher head0.343
Teacher spread0.187 · 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