Seasonal changes in phenolic compounds in the leaves of scab-resistant and susceptible apple cultivars
Bibliographic record
Abstract
Leaf samples were collected from apple varieties susceptible and resistant to apple scab [Venturia inaequalis (Cke.) Wint.] from June to September for 2 yr between 2005 and 2006. These were analyzed for phenolic compounds, using high-performance liquid chromatography (HPLC). In the apple leaves, the following hydroxycinnamic acids were detected: chlorogenic, caffeic and p-coumaric acids; the following dihydrochalcone: phloridzin, and the following flavonoids: epicatechin, catechin, rutin and quercitrin. The total of phenolic compounds in apple leaves was determined spectrophotometrically, using the Folin-Ciocalteu method. During the growing season, the content of phenolic compounds changed and was related to the physiological stage of the tissue and cultivar used. Each phenolic compound had its own curve of seasonal changes in concentration. In 2005 there was no significant change in total phenols during the growing season. In 2006, their content changed significantly during the growing season; statistically the highest content of total phenols was found in the leaves in August. Total phenols as well as single phenolics were statistically higher in resistant than in susceptible apple varieties for both years.Key words: Malus × domestica Borkh., leaves, phenolic compounds, seasonal changes, Venturia inaequalis (Cke.) Wint., resistance
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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 | 0.001 | 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".