Ethnobotanical survey of medicinal plants used in north-central Morocco as natural analgesic and anti-inflammatory agents
Bibliographic record
Abstract
For centuries, the Moroccan population has relied on herbs as medicine to treat a variety of diseases, especially inflammation and pain-related ones. To the best of our knowledge, no survey had ever been conducted to address this subject in the Fez-Meknes region of Morocco. Thus, a survey was conducted of 544 interviewees, using a semi-structured ethnopharmacological survey designed with “Why-How” questions about plants used, their vernacular names, parts used, mode of preparation, and mode of administration. Fidelity level (FL), relative frequency of citation (RFC), frequency of citation (FC), informant consensus factor (ICF), and family importance value (FIV) were calculated. A total of 104 plant species belonging to 49 families used for inflammatory and pain treatment were documented. Lamiaceae (16 species) was the most used family and Curcuma longa L. (RFC=0.069) was the most frequently prescribed by local traditional healers and herbalists. Leaves were the most used part for herbal remedies, appearing in 30.8% of preparations. Decoctions and infusions were the most popular preparation methods with percentages of 38.3% and 19.2%, respectively. Inflammations and pain in the digestive system had the largest widespread affections (IFC= 0.729) in the Fez-Meknes region. The findings of this study uncovered a reliable and original source of ethnomedicinal data pertaining to plants used to treat inflammation and inflammatory pain in the Fez-Meknes region, which could serve as a credible source of knowledge to determine new-based phytomedicines.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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".