Evaluation of the TOM-CAST Forecasting Model in Asparagus for Management of Stemphylium Leaf Spot in Ontario, Canada
Bibliographic record
Abstract
In the last 5 years, asparagus acreage in Canada has increased by over 25%. Stemphylium leaf spot, caused by Stemphylium vesicarium, has emerged as the predominant foliar pathogen of asparagus. Typically, contact fungicides are applied every 14 days; however, regardless of the number of applications, growers are not achieving adequate control of the disease. The TOM-CAST forecasting model is used widely in Michigan asparagus fields, but it has never been assessed for suitability in Ontario or in the popular cultivar, Guelph Millennium. Six field trials were conducted in 2012 and 2013 to evaluate the TOM-CAST forecasting model in two asparagus cultivars. The fungicides chlorothalonil or azoxystrobin/difenoconazole were applied according to the forecasting model or on a 14-day interval. The effectiveness of the forecasting model differed between sites and cultivars. Even though TOM-CAST is used in all cultivars in Michigan, TOM-CAST was not effective on Guelph Millennium. In the cultivar Jersey Giant, however, TOM-CAST with a 20 disease severity value spray interval improved control of Stemphylium leaf spot without increasing the number of sprays, compared with a 14-day treatment. The results in Guelph Millennium differed between sites. At one site, TOM-CAST maintained similar levels of Stemphylium leaf spot, but increased the number of applications, compared with a 14-day application interval. Of more concern, none of the fungicide treatments differed greatly from the untreated control at the other site. Our results show that forecasting models need to be validated locally in asparagus cultivars relevant to production today.
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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 | 0.001 | 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 | 0.001 | 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".