The Fine Scale Ethnotaxa Classification of Millets in Southern India
Bibliographic record
Abstract
This research explores variation in minor millets in the context of traditional knowledge (TK) and scientific knowledge (SK), including ethnobotany genomics, in southern India. In order to perceive biodiversity, we need to take a closer look at the natural variation among species within the context of existing classifications using both TK and SK. Malayali informants of the Kolli Hills in India were surveyed using 174 millet samples. We also collected seeds and grew millets in greenhouse environments from which we recorded 96 morphological characters and extracted DNA for barcoding. Quantitative multivariate classification analysis of these plants revealed that the Malayali millet classification is hierarchical and recognizes considerable fine scale variation with high consensus. In the field, the Malayali classified and consistently identified 19 millet ethnotaxa (landraces). Variation in these same samples was analyzed using morphometric and molecular characters (DNA barcoding) but revealed fewer taxa. Some of the cryptic taxa identified by the Malayali, including a potentially drought tolerant millet ethnotaxa, have considerable nutritional, medicinal, and ecological value.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".