<i>Calea ternifolia</i> Kunth, the Mexican “dream herb”, a concise review
Bibliographic record
Abstract
Calea ternifolia Kunth (Asteraceae), the “dream herb”, is an important medicinal plant that grows from Mexico to Costa Rica. The plant is highly valued for rituals and for treating several illnesses including anorexia, upset stomach, diabetes, periodic fevers, diarrhea, bile problems, and skin diseases. This comprehensive literature survey on C. ternifolia was performed up to January 2021. Our review focuses on traditional uses, botanical aspects, chemical constituents, quality control tests, as well as pharmacological and toxicological studies. Data were recorded using online scientific databases including Scopus, PubMed, Google Scholar, Taylor and Francis Imprints, National Center for Biotechnology Information, Science Direct, JSTOR, and SciFinder. The information was assembled from research articles, relevant books on herbal medicinal plants and the history of medicinal plants from Mexico, theses, reports, and web pages. The more significant botanical and ethnomedical aspects were recorded including the discovery of the oneirogenic use (enhancer of dreams) of C. ternifolia by the Chontal Indigenous communities in Oaxaca, Mexico. The plant contains sesquiterpenes and flavonoids as the major constituents. Some properties associated with the plant’s traditional uses have been demonstrated including spasmolytic, antidiabetic, antidepressant, anti-inflammatory, and antinociceptive effects. The plant’s toxicity will be discussed in this paper. Solid pharmacological research provided evidence supporting the use of the dream herb for oneiromancy.
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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 | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".