Keynote Lecture 1 “New directions in ethnobotany and ethnopharmacology of Turtle Island (North and Central America)”
Bibliographic record
Abstract
Reconciliation with, and empowerment of Indigenous Peoples in North and Central America has led to collaborative ethnobotany and ethnopharmacology research on traditional medicines for safety and efficacy. The Team on Aboriginal Antidiabetic Medicines in collaboration with James Bay Cree Communities of Eeyou Istchee territories studied traditional foods, medicines and lifestyles that can reverse a growing epidemic of diabetes in First Nations communities. More than 51 consensus boreal plants identified by 148 elders were ranked using syndromic importance value. Among these, bioassay guided isolation and metabolomic techniques [ 1 ] identified novel phytochemicals with glucose lowering activity, adipogenic activity, and antiglycation activity. In a metanalysis of results [ 2 ], plants were ranked by Pharmacological Importance Value. Results were used in community public health projects in four First Nations. In a second Indigenous collaboration [ 3 ] with Q’eqchi’ Maya Healers of Xna’ajeb’ aj Ralch’o’och’, Belize, the healers requested scientific assessment of their plants to gain recognition from local government. An ethnobotanical survey identified a tropical medicinal pharmacopoeia of 169 species in several use categories with a high informant consensus factor. Many wild species were cultivated for the first time in an Indigenous garden for primary healthcare. Bioassay guided isolation identified novel anti-anxiety, anti-inflammatory and antiglycation phytochemicals, with several results confirmed in animal models. One plant used dates back to the Classic period [ 4 ]. Despite the distance between them, Cree and Maya healers share a common cosmocentric world view and a belief that their medicines are most effective in a traditional context for healing. Publication History Article published online: 16 November 2023 © 2023. Thieme. All rights reserved. Georg Thieme Verlag KG Rüdigerstraße 14, 70469 Stuttgart, Germany
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.074 | 0.017 |
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".