Enhancing the yield of target tissue and secondary metabolites in Calendula officinalis L., a medicinal plant.
Bibliographic record
Abstract
Medicinal crops can be usefully studied in controlled hydroponic systems in which various factors can be manipulated to increase target plant tissue yield and secondary metabolite production. In this project floral tissue and other plant organs of the medicinal plant Calendula officinalis and four important secondary metabolites: quercetin, rutin, isorhamnetin-3-O-glucoside and isorhamnetin-3-rutinoside were quantified under contrasting conditions in terms of phosphorus concentration, rate of nutrient supply and simulated foliar herbivory in a factorial experimental design. The objectives were to identify conditions that will maximize the yield of target plant tissue, maximize the production of secondary metabolites and minimize the variation in that value. Phosphorus concentration was varied because this nutrient is important for plant growth, particularly during flower production. Nutrient supply rates used in this study sought to minimize nutrient deficiencies and growth fluctuations. Selected plants in the study were also subjected to a clipping treatment, as numerous studies have shown that herbivory can induce increased growth ("overcompensation") and potentially to stimulate secondary metabolite production. (Abstract shortened by UMI.)Dept. of Biological Sciences. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2002 .S74. Source: Masters Abstracts International, Volume: 42-01, page: 0150. Adviser: Lesley Lovett-Doust. Thesis (M.Sc.)--University of Windsor (Canada), 2003.
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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 | 0.001 |
| 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) | 0.002 | 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".