Maple Leaf Extracts to Control Angular Leaf Spot of Cucurbits Caused by <i>Pseudomonas syringae</i>
Bibliographic record
Abstract
ABSTRACT Angular leaf spot (ALS) caused by Pseudomonas syringae is an important seedborne bacterial disease in cucurbits. Currently, growers have very few alternatives to copper‐based pesticides for the control of the disease in both conventional and organic growing systems. Recent work performed by our group revealed that sugar maple and silver maple leaf extracts show potential as alternatives to the use of copper‐based pesticides for the control of plant‐pathogenic bacteria. This study investigated the antibacterial and prophylactic activity of ethanolic sugar maple/silver maple autumn‐shed leaf extracts against P. syringae . Silver maple and sugar maple leaf extracts showed antibacterial activity under in vitro conditions against P. syringae with minimum inhibitory concentration (MIC) values of 0.39 and 0.78 mg/mL, respectively. The extracts were also very effective as seed treatments at concentrations of 50 or 100 mg/mL. Foliar applications of sugar maple leaf extracts at concentrations of 14.1 mg/mL (corresponding to phytotoxic dose 5% [PD5] value) and 28.2 mg/mL (corresponding to 2 × PD5 value) were either as effective (PD5) or more effective (2 × PD5) than copper octanoate to control ALS on cucumber plants. Silver maple leaf extracts used at a concentration corresponding to PD5 value were as effective as copper octanoate to control ALS on cucumber. Foliar applications of the extracts also showed efficacy in reducing the severity of ALS on butternut squash plants. This study opens new avenues for the management of ALS in cucurbits using silver maple and sugar maple leaf extracts as alternatives to the use of copper‐based pesticides.
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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 | 0.000 |
| 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".