Bibliographic record
Abstract
During growth and post-harvest storage, fungi can infect grain and produce secondary metabolites known as “mycotoxins ”. Some mycotoxins are regulated due to their potential hazardous health effects. Thusly, analysis of bulk grain consignments for mycotoxins is common in the grain trade. The heterogeneity of bulk grain with respect to deoxynivalenol (DON) and ochratoxin A (OTA), two regulated mycotoxins, was investigated. Variation of concentrations amongst individual wheat kernels was assessed, along with the variation within sub-samples and test portions produced from 10 kg laboratory samples, and amongst 500 t increments sampled during loading of bulk shipments (4,600 to 55,000 t). Concentrations in individual kernels ranged from < 0.02 to 583 mg/kg for OTA and < 0.3 to 414 mg/kg for DON. Analysis of the distribution of concentrations was limited due to the difference between the sample sets available for use; one was naturally infected (DON) and the other was inoculated and incubated under laboratory conditions (OTA). Bulk shipments were sampled during loading using a Canadian Grain Commission-approved automated cross-stream diverter-type sampler and in-line divider. Increments were combined, and 10 kg laboratory samples were prepared from the resulting composite using a Boerner divider, comminuted using a rotor beater mill, and sub-sampled using rotary sample division to produce representative sub-samples and test portions. Concentrations of OTA in the 500 t increment samples varied from < 0.25 to 22.9 µg/kg; DON varied from < 0.05 to 0.67 mg/kg. Within shipments, the OTA concentrations varied more amongst increments than did DON. The coefficients of variation for OTA ranged from 42 to 95% which were 2-4× greater than for DON. The results illustrate heterogeneity of bulk wheat relevant to international trade and regulated mycotoxins. Differences observed for DON and OTA also reflect how biological differences in mycotoxin production contributes to the challenges faced in analysing bulk whole grain for 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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".