Ethnobotany in the Andes and the Amazon in a world of Nagoya Protocol and post SARS-CoV-2 pandemic
Bibliographic record
Abstract
Plants provide humankind with our most basic resources — food, medicines, fiber, and a whole array of other useful products. Relatives of wild crops and traditional plant varieties have been the foundation of crop domestication, plant breeding, and indeed the whole of modern agriculture. Plants provide the molecular basis of many pharmaceuticals, as direct compounds or as molecular blueprints. Modern science has started to confirm that the distinction between nutrition and medicine is blurred. With economic development empowering a greater percentage of the world’s people, urban areas continuing to expand, and human populations projected to double in the next 50 years, it seems certain that natural resources will face increasing threat. Habitat loss, unsustainable extraction of plants, spread of invasive species, climate change, and other human activities will have tremendous impacts. In this overview, we assess the changes in ethnobotanical research in the Andes and Amazon in the last decades using the Chábobo Ethnobotany Project as an example for modern ethnobotanical research under Convention on Biological Diversity and the attached Nagoya Protocol, and reflect on the possibilities of using this model for future ethnobotanical studies in a post-SARS-CoV-2 world.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".