Agronomic considerations for reducing deoxynivalenol in wheat grain
Bibliographic record
Abstract
Wheat fields under an array of agronomic practices were studied during harvest across southern and eastern Ontario. Mature wheat grain samples were harvested by hand and analyzed for deoxynivalenol (DON). DON levels from wheat grain samples harvested by hand were likely more representative of levels in the field than samples that are typically harvested by machine. The amount of variation in DON levels associated with year and agronomic effects were calculated from simple linear models. As expected, the largest factor associated with variation in DON levels was the year. Year effects accounted for 48% of the variation in DON levels across all fields during 4 years of the survey, followed by cultivar (27%), and the crop 1 year previous to wheat (14–28% depending on the year). No effect on DON could be detected from other agronomic factors including tillage system, crops planted 3 years before wheat, or type of nitrogen fertilizer applied in the spring.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".