Evaluation of Forecasting Models for Fusarium Head Blight of Wheat Under Growing Conditions of Quebec, Canada
Bibliographic record
Abstract
Fusarium head blight (FHB) is a fungal disease of wheat (Triticum aestivum L.) causing frequent economic losses to farmers under growing conditions of Eastern Canada. To assess risks associated with this disease and guide fungicide use decisions, many researchers from numerous countries have developed weather-based forecasting models. This work aims at evaluating which model produces the most accurate predictions of disease infection or deoxynivalenol (DON) content under climatic conditions occurring in Quebec. Spring wheat was grown during two seasons and winter wheat during one season at four experimental sites located in Quebec. Nine selected models for evaluation produced predictions of DON content (Canada and Italy), disease incidence (Argentina and Italy), and probability of epidemics (United States). Data from plots without fungicide (52 samples) were used to test the models listed above. Reliability of the selected forecasting models was evaluated with receiver operating characteristic (ROC) curve analysis. DON content (≥1 ppm) was the best crop damage indicator to differentiate epidemic (cases) and nonepidemic (controls) situations. Two American and the Argentinean forecasting models were more reliable than the others when the thresholds recommended in the literature were adjusted using the results for the ROC curve analyses. Those models are a good starting point for the implementation of an FHB forecasting system adapted to wheat production in Quebec.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".