Genomic valorization of the fine scale classification of small millet landraces in southern India
Bibliographic record
Abstract
Our research seeks to investigate genomic diversity of landraces of millet, addressing a key uncertainty that will provide a framework for (i) a DNA barcode method that could be used for fast, sensitive, and accurate identification of millet landraces, and (ii) millet landrace conservation including biocultural diversity. We found considerable intraspecific variation among 15 landraces representing six species of small millets using nuclear regions (ITS, ITS1, and ITS2); there was no variation in plastid regions (rbcL, matK, and trnH-psbA). An efficacious ITS2 DNA barcode was used to make 100% accurate landrace assignments for 150 blind samples representing 15 landraces. Our research revealed that genomic variation is aligned with a fine-scale classification of landraces using traditional knowledge (TK) of local farmers. The landrace classification was highly correlated with traits (morphological, agricultural, and cultural utility) associated with considerable factors such as yield, drought tolerance, growing season, medicinal properties, and nutrition. This could provide a DNA-based model for conservation of genetic diversity and the associated bicultural diversity (TK) of millet landraces, which has sustained marginal farming communities in harsh environments for many generations.
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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 | 0.000 |
| Scholarly communication | 0.000 | 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".