Classifying aggressiveness on common bean (<i>Phaseolus vulgaris</i> L.) of a Canadian collection of <i>Sclerotinia sclerotiorum</i> L. de Bary isolates
Bibliographic record
Abstract
Sclerotinia sclerotiorum L. de Bary (Ss) is one of the most destructive pathogens in Canada and around the world. It affects over 500 species of plants, including the economically important common bean. In Canada, its impact is pronounced, necessitating a comprehensive exploration of its diversity and aggressiveness. This study aimed to elucidate the diversity and aggressiveness of Ss isolates collected from commercial fields in three Canadian provinces. Through a dual phenotypic trait analysis of Mycelial Compatibility Groups (MCGs) and aggressiveness determination, we investigated 39 Ss isolates from an interprovincial set and 30 samples from adjacent fields referred to as the proximal subset. Our investigation of MCGs revealed the presence of 18 distinct MCGs in the interprovincial set, suggesting a set of samples with high diversity. Conversely, proximal fields exhibited more clonal behaviour, characterized by only two MCGs. A novel classification system for MCGs based on geographical dispersal and isolate frequency was proposed, delineating Core, Regional, and Endemic MCGs. Aggressiveness testing identified that 82.35% of isolates displayed aggressive responses. In contrast, 17.65% showed mildly aggressive isolates, shedding light on the threat that Ss poses to current commercial fields by displaying predominantly aggressive behaviour among the isolates in the set of samples of study. These phenotypic analyses highlight the complex interactions between Ss isolates and common bean cultivars, providing valuable information for understanding the pathogen’s behaviour. The observed disparity in diversity between interprovincial and proximal fields hints at varied evolutionary pressures, possibly influenced by geographic isolation and agricultural practices. This study outlines MCG and aggressiveness reactions of Ss isolates.Proposed classification system for MCGs enables comparative studies.High diversity in Ss isolates from three Canadian Provinces categorized in 18 MCGs. This study outlines MCG and aggressiveness reactions of Ss isolates. Proposed classification system for MCGs enables comparative studies. High diversity in Ss isolates from three Canadian Provinces categorized in 18 MCGs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".