Pollination system and deficit irrigation affect flavonolignan components of sylimarin, oil, and productivity of milk thistle
Bibliographic record
Abstract
Abstract The biosynthesis and accumulation of secondary metabolites in plant tissues interact strongly with environmental conditions and breeding systems. Limited knowledge is available on the effects of breeding system (self vs. open pollination) and deficit irrigation on the composition of flavonolignans, seed yield, and oil content of different genotypes of milk thistle (Silybum marianum L.). Four ecotypes of Iranian milk thistle collected from diverse geographical regions were each self‐ and open‐pollinated; they were then assessed in the field under both normal and deficit irrigation for seed yield, oil percentage, silymarin, and its components during 2014 and 2015. Deficit irrigation decreased seed yield and silybin A but increased total silymarin content, silybin B, and silydianin. In both moisture environments, seed yield was positively associated with oil percentage and silybin A. Open pollination improved seed yield, oil percentage, total silymarin, and its components in milk thistle ecotypes as compared with self‐pollination. Considerable genetic variability was observed among the evaluated ecotypes in their response to moisture environments and pollination status. Under open pollination, the northern Iranian ecotypes Mashhad and Sari were identified as promising varieties for further studies.
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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".