Plant health and yield of dry bean not affected by strobilurin fungicides under disease-free or simulated hail conditions
Bibliographic record
Abstract
Mahoney, K. J. and Gillard, C. L. 2014. Plant health and yield of dry bean not affected by strobilurin fungicides under disease-free or simulated hail conditions. Can. J. Plant Sci. 94: 1385–1389. Strobilurin fungicides have been advocated to manage plant stress, optimize plant health, and increase yields of several crops. Studies were conducted in 2006, 2007, 2008, and 2009 to determine if azoxystrobin or pyraclostrobin induced plant health effects and increased yield of dry bean grown under conditions with very low levels of foliar disease and with the application of a simulated hail stress treatment. Azoxystrobin (125 g a.i. ha−1) and pyraclostrobin (100 g a.i. ha−1) applied at the start of flowering reduced the percentage of harvested dry bean seeds that were discoloured or misshaped (i.e., pick value) to 2.06 and 2.15%, respectively compared with 2.4% for the untreated control. Increased seed quality of edible legumes has been identified as a plant health benefit induced by strobilurin fungicides; however, neither fungicide contributed to increased seed weight or dry bean yield compared with the untreated control in the presence or absence of a simulated hail treatment. The results of this study suggest that the application of azoxystrobin or pyraclostrobin when dry bean growing conditions are unfavourable for disease development should be weighed against economic considerations and the potential risks associated with disease resistance development.
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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) | 0.001 | 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) | 0.001 | 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".