A single molecular marker to distinguish between species of <i>Dioscorea</i>
Bibliographic record
Abstract
Yams are species of the genus Dioscorea (family Dioscoreaceae), which consists of approximately 630 species. The majority of the world production of yams occurs in Africa with 58.8 million t annually, but they are also produced in the Americas and Asia. The saponins in yams have been reported to possess various properties to improve health. The tuber and aerial parts of various species often share morphological similarities, which can cause problems in the proper identification of sample material. For example, the rootstocks and aerial parts of Dioscorea villosa L. share similarities with Dioscorea polystachia Turcz. Dioscorea bulbifera L. may be mistaken for Dioscorea alata L. owing to similar morphologies. Various molecular analyses have been published to help with the identification of species and varieties within the genus Dioscorea. The multi-loci or single-locus analysis has resulted in varying success, some with only a limited discrimination rate. In the present study, a single nuclear genomic region, biparentally inherited, was analyzed for its usefulness as a molecular marker for species identification and discrimination between D. bulbifera, D. villosa, D. nipponica, D. alata, D. caucasica, and D. deltoidea samples. The results of this study show that the LFY genomic region can be useful as a molecular marker to distinguish between samples.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".