Exploration and characterization of bioactive phytochemicals in native Canadian plants for human health
Bibliographic record
Abstract
The boundary between medicine and food is not always clear in many ancient cultures. Many plants have traditionally been used in both culinary and healing practices. Herbs, in particular, have shown this dual functionality. Scientific information on herbal medicines has been limited to exotic plants, and only a few herbal plants native to, or grown in, Canada, such as American ginseng, Echinacea, St. John’s wort and feverfew, have been studied. Thorough investigations have not been carried out, and there is a lack of information about native Canadian plants and their potential as medicinal plants, particularly in terms of their chemical composition, biological activity and potential use for disease prevention. Also, from the marketing point of view, many of the existing herbs have only a small niche in the marketplace, so over production and consequent price depression can easily happen, as seen in the ginseng industry. There is obviously a need for multidisciplinary collaboration among herbalists, botanists, chemists and other scientists, since introducing native plants into mass production requires knowledge of environmental impact, genetic variability and the effects of other factors on the bioactive components. This review is intended to introduce the needs, techniques and challenges of such an approach with an emphasis on chemical and biochemical characterizations. Key words: Phytochemicals, native plants, medicinal plants, aboriginal plants
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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 | 0.001 | 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".