Sugar maple autumn-shed leaf extract: a potential antibacterial agent for the management of lettuce bacterial leaf spot (<i>Xanthomonas campestris</i> pv. <i>vitians</i>) and varnish spot (<i>Pseudomonas cichorii</i>)
Bibliographic record
Abstract
Different extracts prepared from wastes (barks, branches, needles, or leaves) of different trees (grey alder, balsam fir, American larch, red maple, sugar maple, white spruce, black spruce, jack pine, white pine, quaking aspen, sweet cherry, and northern red oak) were investigated for their potential use as antibacterial agents for the management of lettuce varnish spot and bacterial leaf spot caused by Pseudomonas cichorii (Swingle) Stapp and Xanthomonas campestris pv. vitians (Brown) Dye, respectively. Extracts were first screened for their antibacterial activities against P. cichorii and X. campestris pv. vitians using the in vitro disk diffusion assay. Based on the diameter of the inhibition zone, ethanol (95%) extract prepared from sugar maple autumn-shed leaves (SMASL) and aqueous ethanol (50%, v/v) extracts prepared from SMASL and from sugar maple green leaves showed the strongest antibacterial activities. Ethanol (95%) SMASL extract was further investigated for its efficacy to manage bacterial diseases when applied on lettuce plants grown in the greenhouse. Foliar application of ethanol (95%) SMASL extract at a concentration of 3.2 g L−1 was shown to significantly (P ≤ 0.05) reduce bacterial leaf spot severity compared with the control without causing phytotoxicity symptoms that could prevent the commercial marketing of the lettuce. Ethanol (95%) SMASL extract (1.6 and 3.2 g L−1) was also shown to significantly reduce varnish spot severity in one experiment out of two. This study identifies for the first time the possibility of exploiting SMASL to manage bacterial diseases affecting horticultural crops.
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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".