Using Weather Variables Pre- and Post-heading to Predict Deoxynivalenol Content in Winter Wheat
Bibliographic record
Abstract
Substantial economic losses have occurred because of unacceptable concentrations of deoxynivalenol (DON) in wheat. Accurate predictions of DON in mature grain at wheat heading are needed to make decisions on whether a control strategy is needed. Our objective was to identify important weather variables, and their timing, for predicting concentrations of DON in mature grain at wheat heading. We measured the concentration of DON in 399 farm fields in southern Ontario, Canada, from 1996 to 2000. DON varied in field samples from undetectable to over 29 μg g-1. Weather variables, such as daily rainfall, daily minimum and maximum air temperatures, and hourly relative humidity, were estimated for each field from nearby weather stations and were normalized to the date of 50% head emergence. Stepwise multiple regression procedures determined the most important weather variables and their timing around heading. DON was responsive to weather in three critical periods around heading. In the first period, 4 to 7 days before heading, DON generally increased with the number of days with >5 mm of rain and decreased with the number of days of <10°C. In the second period, 3 to 6 days after heading, DON increased with the number of days of rain >3 mm and decreased with days exceeding 32°C. In the third period, 7 to 10 days after heading, DON increased with number of days with >3 mm of rain. A relationship between relative humidity and DON was not detected. Overall, 73% of the variation in the concentration of DON was explained by using weather from all three critical periods. Concentrations of DON <2.0 μg g-1 were predicted best; in fact, concentrations of DON of <1.0 μg g-1 were predicted correctly on over 89% of the fields used to train the model.
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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) | 0.000 | 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".