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Record W4403533330 · doi:10.1080/19440049.2024.2417394

Contaminants and residues have varied distributions in large volumes of wheat

2024· article· en· W4403533330 on OpenAlex
Sheryl A. Tittlemier, Richard Blagden, Jason Chan, Dainna Drul, Don Gaba, Mei Huang, Anja Richter, Mike Roscoe, Maria Serda, Valentina Timofeiev, Michael Tran

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueFood Additives & Contaminants Part A · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMycotoxins in Agriculture and Food
Canadian institutionsUniversity of ManitobaGovernment of Canada
FundersMcMaster University
KeywordsEnvironmental scienceEnvironmental chemistryContaminationAgronomyChemistryBiologyEcology

Abstract

fetched live from OpenAlex

Analysis of bulk wheat consignments for naturally-occurring contaminants and residues from plant protection products is common, and helps manage potential health risks to consumers. The heterogeneous distribution of some mycotoxins in wheat has been described, however the distribution of other contaminants and residues has not yet been reported. This study characterized distributions of deoxynivalenol, ochratoxin A, ergot alkaloids, cadmium, and glyphosate in nine large consignments of wheat by analysing composite samples representing sub-lots prepared from increments obtained during the entire loading process. The widest span of concentrations within a consignment occurred for ochratoxin A (<0.5-22.9 µg/kg) and ergot alkaloids (0.009-0.486 mg/kg), followed by deoxynivalenol (<0.05-0.76 mg/kg) and glyphosate (<0.3-5.01 mg/kg), and then cadmium (0.022-0.102 mg/kg). Experimental semivariograms were plotted to model the spatio-temporal correlation of analytes within consignments during loading. Analyses demonstrated that distributions of contaminants and residues within a particular consignment differed, and that distributions of a particular contaminant or residue differed among consignments. The results indicate that sampling during only a portion of a loading or unloading process can result in a composite sample that is not representative of the consignment and thus increase the risk of misclassifying a consignment as compliant.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.593
Threshold uncertainty score0.557

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 head0.017
GPT teacher head0.241
Teacher spread0.224 · 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