Applications in forecasting deoxynivalenol in wheat using DONcast
Bibliographic record
Abstract
Forecasting deoxynivalenol is useful to help prevent entry of the toxin into the food chain and for growers to achieve a higher potential of profits with quality grain at harvest. Wheat fields under an array of agronomic practices were sampled for deoxynivalenol content at harvest across Ontario from 1996 to 2004. A robust prediction model was developed and commercialised for wheat under the name of DONcast. This model is delivered online. There is growing interest amongst producers to use this tool pre-harvest to make marketing decisions and for grain handlers to use the tool for grain sourcing, in addition to fungicide application decisions at heading. Validations of the model have been successful in 4 countries, including most recently France and Uruguay. It is well known that deoxynivalenol predictions are very sensitive to coincidental weather around heading, varietal susceptibility to Fusarium and deoxynivalenol accumulation, and to the management of previous crop residue. DONcast has successfully taken these factors into account, and we have demonstrated its robustness across varied environments.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".