Ethnobotany genomics – use of DNA barcoding to explore cryptic diversity in economically important plants
Bibliographic record
Abstract
The ethnobotany genomics concept is founded on the idea of ‘assemblage’ of biodiversity knowledge. This includes a coming together of different ways of knowing and valorizing species variation in a novel approach seeking to add value to both traditional knowledge (TK) and scientific knowledge (SK). Ethnobotany genomics is defined as exploring the variation in genomic sequences from many species, and here we present some of our recent work that demonstrates the potential benefits of this approach for ethnobotanical research with economic implications. DNA barcoding was used to identify Acacia and nutmeg taxa that are economically important to society-at-large. Furthermore we identified considerable variation that is recognized by several indigenous cultures. The impacts of ethnobotany genomics will extend well beyond biodiversity science. Explorations of the genomic properties across the expanse of life are now possible using DNA barcoding to assemble sequence information for a standard portion of the genome from large assemblages of species. Perhaps the most important contribution is major barcode projects will leave an important legacy; a comprehensive repository of highquality DNA extracts that will facilitate future genomic investigations. Keywords: DNA barcoding, economic botany, biodiversity, plant diversity, ethnopharmacology, ethnomedicine, ethnobiology, biotechnology
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".