Contaminants and residues have varied distributions in large volumes of wheat
Bibliographic record
Abstract
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".