Ethnobotany genomics - discovery and innovation in a new era of exploratory research
Bibliographic record
Abstract
We present here the first use of DNA barcoding in a new approach to ethnobotany we coined "ethnobotany genomics". This new approach is founded on the concept of 'assemblage' of biodiversity knowledge, which 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). We employed contemporary genomic technology, DNA barcoding, as an important tool for identifying cryptic species, which were already recognized ethnotaxa using the TK classification systems of local cultures in the Velliangiri Hills of India. This research is based on several case studies in our lab, which define an approach to that is poised to evolve quickly with the advent of new ideas and technology. Our results show that DNA barcoding validated several new cryptic plant species to science that were previously recognized by TK classifications of the Irulas and Malasars, and were lumped using SK classification. The contribution of the local aboriginal knowledge concerning plant diversity and utility in India is considerable; our study presents new ethnomedicine to science. Ethnobotany genomics can also be used to determine the distribution of rare species and their ecological requirements, including traditional ecological knowledge so that conservation strategies can be implemented. This is aligned with the Convention on Biological Diversity that was signed by over 150 nations, and thus the world's complex array of human-natural-technological relationships has effectively been re-organized.
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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 | 0.042 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.005 | 0.063 |
| Scholarly communication | 0.013 | 0.025 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".