A Multi-Year Study of Mycotoxin Co-Occurrence in Wheat and Corn Grown in Ontario, Canada
Bibliographic record
Abstract
Mycotoxin emergence and co-occurrence trends in Canadian grains are dynamic and evolving in response to changing weather patterns within each growing season. The mycotoxins deoxynivalenol and zearalenone are the dominant mycotoxins detected in grains grown in Eastern Canada. Two potential emerging mycotoxins of concern are sterigmatocystin, produced by Aspergillus versicolor, and diacetoxyscirpenol, a type A trichothecene produced by a number of Fusarium species. In response to a call from the 83rd Joint Expert Committee on Food Additives and Contaminants, we conducted a comprehensive survey of samples from cereal production areas in Ontario, Canada. Some 159 wheat and 160 corn samples were collected from farms over a three-year period. Samples were extracted and analyzed by LC-MS/MS for 33 mycotoxins and secondary metabolites. Ergosterol was analyzed as an estimate of the overall fungal biomass in the samples. In wheat, the ratio of DON to its glucoside, deoxynivalenol-3-glucoside (DON-3G), exhibited high variability, likely attributable to differences among cultivars. In corn, the ratio was more consistent across the samples. Sterigmatocystin was detected in some wheat that had higher concentrations of ergosterol. Diacetoxyscirpenol was not detected in either corn or wheat over the three years, demonstrating a low risk to Ontario grain. Overall, there was some change to the mycotoxin profiles over the three years for wheat and corn. Ongoing surveys are required to reassess trends and ensure the safety of the food value chain, especially for emerging mycotoxins.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".