Interaction of Monoterpenoids, Methyl Jasmonate, and Ca2+ in Controlling Postharvest Brown Rot of Sweet Cherry
Bibliographic record
Abstract
The banning of synthetic fungicides for postharvest use on fruits in Canada has prompted a search for alternative control strategies for postharvest brown rot caused by Monilinia fructicola (Wint.) Honey on sweet cherry ( Prunus avium L.). Thymol and carvacrol were the two most potent fungicides among the monoterpenoids tested. The brown rot incidences of M. fructicola -inoculated cherry dipped in 1000 μg·mL -1 thymol and carvacrol were 24% and 23%, respectively, compared with 81% for the control. The effects of thymol and carvacrol were not significantly enhanced by the addition of CaCl 2 or CaB'y®, a foliar calcium fertilizer. Decco® 282 significantly reduced the activity of thymol. Methyl jasmonate, an elicitor of plant defense mechanisms, did not reduce brown rot by itself, and did not increase the efficacy of thymol and carvacrol when used as an additive in dipping or fumigation experiments. Thymol and carvacrol caused stem browning of cherry fruits in the fumigation experiment, however, 69% and 73%, respectively, of the browning was prevented when methyl jasmonate was used as a co-fumigant. Chemical names used: 5-methyl-2-(1-methylethyl)phenol (thymol); 2-methyl-5-(1-methylethyl)phenol (carvacrol); methyl 3-oxo-2-(2-pentenyl)cyclopentane acetate (methyl jasmonate).
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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 | 0.000 |
| 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".