Occurrence of Ascospores and White Mold Caused by <i>Sclerotinia sclerotiorum</i> in Dry Bean Fields in Alberta, Canada
Bibliographic record
Abstract
L.) production in Canada. Disease forecasting is one tool that could help growers manage the disease while reducing fungicide use. However, predicting white mold epidemics has remained difficult due to their sporadic occurrence. In this study, over the course of four growing seasons (2018 to 2021), we surveyed dry bean fields in Alberta and collected daily in-field weather data and daily in-field ascospore counts. White mold levels were variable and generally high in all years, confirming that the disease is ubiquitous and a constant threat to dry bean production. Ascospores were present throughout the growing season, and mean ascospore levels varied by field, month, and year. Models based on in-field weather and ascospore levels were not highly predictive of final disease incidence in a field, suggesting that environment and pathogen presence were not limiting factors to disease development. Rather, significant effects of market class on disease were found, with pinto beans, on average, having the highest disease incidence (33%) followed by great northern (15%), black (10%), red (6%), and yellow (5%). When incidence of these market classes was modeled separately, different environmental variables were important in each model; however, average wind speed was a significant variable in all models. Taken together, these findings suggest that white mold management in dry bean should focus on fungicide use, plant genetics, irrigation management, and other agronomic factors.
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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 | 0.001 | 0.001 |
| 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".