Efficacy of the core DNA barcodes in identifying processed and poorly conserved plant materials commonly used in South African traditional medicine
Bibliographic record
Abstract
Medicinal plants cover a broad range of taxa, which may be phylogenetically less related but morphologically very similar. Such morphological similarity between species may lead to misidentification and inappropriate use. Also the substitution of a medicinal plant by a cheaper alternative (e.g. other non-medicinal plant species), either due to misidentification, or deliberately to cheat consumers, is an issue of growing concern. In this study, we used DNA barcoding to identify commonly used medicinal plants in South Africa. Using the core plant barcodes, matK and rbcLa, obtained from processed and poorly conserved materials sold at the muthi traditional medicine market, we tested efficacy of the barcodes in species discrimination. Based on genetic divergence, PCR amplification efficiency and BLAST algorithm, we revealed varied discriminatory potentials for the DNA barcodes. In general, the barcodes exhibited high discriminatory power, indicating their effectiveness in verifying the identity of the most common plant species traded in South African medicinal markets. BLAST algorithm successfully matched 61% of the queries against a reference database, suggesting that most of the information supplied by sellers at traditional medicinal markets in South Africa is correct. Our findings reinforce the utility of DNA barcoding technique in limiting false identification that can harm public health.
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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 | 0.002 | 0.005 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".