Multi-locus DNA barcoding identifies <i>matK</i> as a suitable marker for species identification in <i>Hibiscus</i> L.
Bibliographic record
Abstract
The genus Hibiscus L. includes several taxa of medicinal value and species used for the extraction of natural dyes. These applications require the use of authentic plant materials. DNA barcoding is a molecular method for species identification, which helps in reliable authentication by using one or more DNA barcode marker. In this study, we have collected 44 accessions, representing 16 species of Hibiscus, distributed in the southern peninsular India, to evaluate the discriminatory power of the two core barcodes rbcLa and matK together with the suggested additional regions trnH-psbA and ITS2. No intraspecies divergence was observed among the accessions studied. Interspecies divergence was 0%-9.6% with individual markers, which increased to 0%-12.5% and 0.8%-20.3% when using two- and three-marker combinations, respectively. Differentiation of all the species of Hibiscus was possible with the matK DNA barcode marker. Also, in two-marker combinations, only those combinations with matK differentiated all the species. Though all the three-marker combinations showed 100% species differentiation, species resolution was consistently better when the matK marker formed part of the combination. These results clearly showed that matK is more suitable when compared to rbcLa, trnH-psbA, and ITS2 for species identification in Hibiscus.
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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".