Review: Northern Ontario medicinal plants
Bibliographic record
Abstract
Hassan, H. M., Jiang, Z.-H., Syed, T. A. and Qin, W. 2012. Review: Northern Ontario medicinal plants. Can. J. Plant Sci. 92: 815-828. The majority of scholarly investigations conducted in the 20th century have provided the incentive for establishing plants as sources of diverse phytochemicals. With infectious and cancerous diseases causing millions of mortalities worldwide, and the advent of resistant strains, the discovery of new antimicrobial and anticancer agents is crucial. Hence, included in this review is a novel list of 48 northern Ontario medicinal plants that may be sources of antifungal, antibacterial and/or anticancer phytochemicals. A total of two ferns and allied plants, two sedges and grasses, six trees, four shrubs, one vine and 33 herbs were identified. These plants were accumulated through interviews with native Elders and a survey of ethnobotanical literature on northern Canadian species of medicinal plants. We also present a critical review of their potential constituents, medicinal properties, and analysis of four promising plants (skullcaps, devil's club, St. John's wort and evergreens). Skullcaps and St. John's wort are model plants with documented anticancer, antibacterial and antifungal bioactivities. However, a considerable gap in ethnopharmacological data was found for species of skullcaps (Scutellaria galericulata, S. parvula and S. lateriflora) and St. John's wort (Hypericum mutilum, H. majus, H. canadense) growing in northern Ontario. These findings provide promising incentives in the ethnopharmacological community for medicinal research in this region.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".