Agronomic and environmental impacts on concentrations of deoxynivalenol and fumonisin B<sub>1</sub>in corn across Ontario
Bibliographic record
Abstract
Relative effects of year, weather, and cultural practices on concentrations of deoxynivalenol (DON) and fumonisin B1 (FB1) were compared in grain that was harvested from corn fields across Ontario, Canada, from 1993 to 2000. Overall, between 59% and 88% of grain samples were contaminated with DON ≥ 0.2 μg g−1 in each year of the survey. From 1997 to 2000, between 17% and 56% of samples were contaminated with FB1 = 1.0 μg g−1, which is higher than first reported for fumonisins in 1993 in Ontario (9%). Deoxynivalenol and FB1 were mostly associated with corn hybrid and year-to-year effects caused by weather or geographical differences in the 8-year study. Among all cultural practices, corn hybrid was the most influential for both DON and FB1 accumulation, accounting for 25% (P < 0.0001) of the variation in both toxins across years. The effect due to year (or to weather, perhaps) accounted for 12% (P < 0.0001) of the variation in concentration of DON and 19% of the variation in FB1. When the effects of hybrid and year were considered in the same model, 42% of the variability of both toxins was accounted for in the model. A higher incidence of DON and fumonisin was detected for corn grown after wheat than for corn after corn. These results demonstrate that predictive models for DON in corn need to include the sensitivity of corn hybrids to infection by Fusarium spp. or mycotoxin accumulation, in addition to the response caused by weather. In practice, the best chance for corn growers to reduce DON and FB1 in harvested grain, when comparing cultural practices, is to select hybrids that are known to have more resistance to fusarium ear rots or mycotoxin accumulation than others.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".