DNA barcoding discriminates a new cryptic grass species revealed in an ethnobotany study by the hill tribes of the Western Ghats in southern India
Bibliographic record
Abstract
Our research brought together traditional aboriginal knowledge (TK) and scientific knowledge (SK) to explore the relationship between scientific and aboriginal systems of botanical classification and the corresponding valorization(s) of biological diversity in the Western Ghats of southern India. We worked with two aboriginal cultures namely 'Irulas' and 'Malasars' of the Nilgiri Biosphere Reserve with an objective of evaluating the ability of different knowledge systems (SK and TK) to distinguish grass species belonging to the genus Tripogon, and assess the ability of DNA barcoding to discriminate a new cryptic species 'Tripogon cope' as deciphered by the hill tribes. We discovered that the aboriginal informants identified a common ethnotaxa 'Sunai pul', which is a cryptic species of grass not recognized by the SK classification.'sunai pul' is very important to both aboriginal cultures with ritualistic and economic utility. Morphometric analysis confirms the cryptic nature of this new species, which was validated using DNA barcoding. DNA barcode regions matK and trnH-psbA showed distinct sequence variations among the closely related ethnotaxa. Given the cryptic nature of ethnotaxa, we propose that a DNA barcode may be a reliable tool to identify ethnotaxa. We have initiated further studies in other cultures to develop theoretically sophisticated insights concerning the encounter between 'local' and 'scientific' approaches to the use of biodiversity knowledge. Furthermore, the research will add to a unifying global effort to speed up the documentation and understanding of the planet's natural diversity, while simultaneously respecting the cultural heterogeneity as a vital component of biological diversity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".