An ethnobotanical meta-analysis of North American medicinal Asteraceae
Bibliographic record
Abstract
The Asteraceae is the largest family of plants in North America and is widely used as medicine by Indigenous peoples. This study investigated the medicinal ethnobotany of North American Asteraceae to identify taxa that appear preferentially selected or avoided for general and specific medicinal uses. Asteraceae-specific ethnobotanical reports recorded in the Native American Ethnobotany database were compiled and, using residual and binomial analyses, 14 tribes were compared and ranked as either over- or under-selected for medicine, food, or technology, and for different categories of medicinal applications. Statistical analysis supported the hypothesis that the selection of species for ethnobotanical purposes is non-random and does not depend on the size of the flora. The Anthemideae tribe was identified as over-selected for all types of applications, including most therapeutic categories, most significantly as pulmonary and orthopedic aids. Subsequent analysis revealed that the over-representation of this tribe was attributed mainly to Achillea millefolium L. and Artemisia spp. The significance of Anthemideae, particularly of Achillea and Artemisia species as highly selected medicinal taxa, emphasizes their cultural importance to Indigenous North Americans. Residual and binomial statistics generally provided parallel results, but supplementary statistical methods, further in-depth investigation of other use categories, and inclusion of plant distribution data may provide greater insight into traditional uses of Asteraceae in North America.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.014 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".