Temporal Dynamics of <i>Botrytis cinerea</i> and <i>Sclerotinia sclerotiorum</i> in Seed Alfalfa Fields of Southern Alberta, Canada
Bibliographic record
Abstract
Blossom blight of seed alfalfa (Medicago sativa L.), caused by the fungal pathogens Botrytis cinerea and Sclerotinia sclerotiorum, is a potentially devastating disease on the Canadian Prairies in moist growing seasons. Monitoring the airborne spore concentrations of these pathogens could facilitate disease forecasting in the region. Nineteen seed alfalfa fields in southern Alberta, Canada were assessed throughout the growing seasons of 2014 and 2015. Trace levels of blossom blight symptoms were found in both years; however, plated floret and pod samples indicated that, overall, B. cinerea increased over the growing season whereas S. sclerotiorum decreased. In both seasons, Burkard 7-day volumetric spore samplers collected daily aerosol samples, and weather stations recorded environmental variables in three fields. Conidia and ascospores collected were quantified each day with a real-time polymerase chain reaction assay. Spore quantification indicated that both B. cinerea and S. sclerotiorum numbers remained low in July and increased in August. Both species had multiple days with high spore discharge, with seasonal maxima of 21,137 conidia and 2,265 ascospores. Exploratory model development indicated that spore discharge of both fungi is associated with environmental stressors such as large changes in relative humidity or high temperatures on preceding days.
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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 | 0.000 | 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".