Response of dry bean white mould [<i>Sclerotinia sclerotiorum</i> (Lib.) de Bary, causal organism] to fungicides
Bibliographic record
Abstract
Mahoney, K. J., McCreary, C. M. and Gillard, C. L. 2014. Response of dry bean white mould [ Sclerotinia sclerotiorum (Lib.) de Bary, causal organism] to fungicides. Can. J. Plant Sci. 94: 905–910. White mould [Sclerotinia sclerotiorum (Lib.) de Bary, causal organism] is a serious disease of dry bean (Phaseolus vulgaris L.). Producers frequently rely on preventative fungicides, yet, this represents a significant production cost. Studies were conducted in 2009, 2010, and 2011 to determine fungicide efficacy under conditions optimized for white mould. Disease incidence×severity ratings were used to calculate the area under the disease progress curve (AUDPC). After harvest, a return on investment (ROI) was calculated. AUDPC values were similar across boscalid, fluazinam, fluopyram, and thiophanate-methyl and lower than Bacillus subtilis, CaCl2, and the untreated. Yield was greatest for fluazinam and fluopyram, followed by thiophanate-methyl. Bacillus subtilis and CaCl2 yields were similar to the untreated. In early-planted environments, using fluazinam and fluopyram resulted in a $500 ha−1 greater ROI than the untreated with $554 gained for every tonne per hectare of dry bean harvested. In late-planted environments, fluazinam and fluopyram averaged about $200 ha−1 greater ROI than the untreated and $494 was returned for every tonne per hectare of dry bean. When dry bean yield potential is reduced under conditions of increased white mould severity, profit margins become narrowed and producers may have to select fungicides that are both efficacious and economical.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".