Forecasting diseases caused by<i>Sclerotinia</i>spp. in eastern Canada: fact or fiction?
Bibliographic record
Abstract
Diseases caused by Sclerotinia sclerotiorum and Sclerotinia minor are responsible for economically important losses on several crops in eastern Canada, including canola, cabbage, carrots, celery, lettuce, snap beans, soybeans, and white beans. For crops such as cabbage and celery, the etiology of these diseases is known, but little information is available on the epidemiology. In these crops, disease avoidance and cultural practices are the primary methods of disease management, although fungicides are sometimes applied after symptoms are observed. For other crops such as beans, canola, carrots, lettuce, and soybean, the epidemiology has been described and at least partially quantified. Based on these epidemiological studies, disease-forecasting systems have been developed for canola, lettuce, and beans, and another is currently being developed for carrots. Epidemics in snap beans are associated with ascospores infecting petals as the primary inoculum, and forecasting is based on soil moisture, rainfall, crop flowering, canopy enclosure, and apothecia. Epidemics in carrots are bicyclic and represent a different situation. Epidemics in the field are associated with infection on senescing leaves in contact with moist soil under the carrot canopy. Forecasting is based on soil moisture, canopy enclosure, senescing leaves, air and soil temperature, and the presence and number of apothecia. Epidemics in storage are associated with air temperature, rate of cooling, surface wetness, and preexisting infection. Despite the availability of forecasting systems for diseases caused by Sclerotinia spp. on several crops, there are no examples of organized monitoring or forecasting programs for these diseases in eastern Canada. Anecdotal comments suggest that the reasons for the lack of development and implementation of forecasting models include the variable severity of epidemics, a lack of registered fungicides, little or no infrastructure to deliver disease-forecasting systems, and declining prevalence of integrated pest management (IPM) programs.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".