Cultivation of rue (<i>Ruta graveolens</i> L., Rutaceae) for the production of furanocoumarins of therapeutic value
Bibliographic record
Abstract
Common rue (Ruta graveolens L.) synthesizes furanocoumarins, which are used in dermatology. The production of these molecules requires the improvement of cultural techniques so as to provide plant material with a high content and (or) yield of furanocoumarins for the pharmaceutical extraction industry. Two experiments were set up, firstly, to improve our understanding of the production of these secondary metabolites by the plant and, secondly, to study the influence of successive cuts on their synthesis. The furanocoumarin content was dependent on the proportion of leaves and fruits on the plant. Conversely, it was independent of the biomass at a given age. The shoots harvested 3 months after sowing had a high furanocoumarin content, as did the fruits in the 2nd year (in both cases about 0.9% of the dry matter). However, the dry matter yield produced was low (0.2 and 1.6 tonnes·ha-1, respectively). The harvest of the shoots in the 2nd year gave a high yield (about 5 tonnes·ha-1) but had a lower furanocoumarin content (0.4%). A system of successive cuts (three cuts in the 2nd year) enabled harvesting to be spread out. The plant material then contained 0.5% furanocoumarins, for 3.3 tonnes of dry matter harvested. The proportion of different furanocoumarins varied according to year and plant parts.Key words: Ruta graveolens L., furanocoumarin, cultivation, cuts, secondary metabolite, Rutaceae.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".