Changes in sugars and phenolics concentrations of Williams pear leaves during the growing season
Bibliographic record
Abstract
Leaves of Williams pear were collected during the growing season from May to October and the contents of sugars and phenolic compounds were analyzed by high-performance liquid chromatography method. Sorbitol was the major sugar (up to 83.8 g kg-1 DW), followed by sucrose (up to 22.1 g kg-1 DW). Concentrations of glucose and fructose were as high as 12.9 and 9.0 g kg-1 DW, respectively. Leaves contained up to 29 471.9 mg kg-1 DW of chlorogenic acid, followed in concentration by rutin (up to 6789.2 mg kg-1 DW), epicatechin (up to 7378.0 mg kg-1 DW), catechin (up to 3846.5 mg kg-1 DW), vanillic acid (up to 1832.1 mg kg-1 DW), syringic acid (up to 1123.5 mg kg-1 DW), caffeic acid (up to 122.5 mg kg-1 DW) and sinapic acid (up to 94.1 mg kg-1 DW). The significant differences in concentration of sorbitol, sucrose, glucose, and in all analyzed phenolics were observed during the growing season (six sampling dates). The lowest concentrations in the leaf were found at the beginning of the growing season in May and June. The highest contents of sugars were in October, with the exception of sorbitol. During the growing season, total phenolic content first increased, then declined. Chlorogenic acid, rutin and caffeic acid contents increased until July, vanillic acid and sinapic acid until August, and catechin, epicatechin and syringic acid until September. However, total phenolic content dropped by 50% from September to October. Key words: Pear leaves, sugars, phenolics, growing season
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".