Exposing the illegal trade in cycad species (Cycadophyta: <i>Encephalartos</i>) at two traditional medicine markets in South Africa using DNA barcoding
Bibliographic record
Abstract
Species in the cycad genus Encephalartos are listed in CITES Appendix I and as Threatened or Protected Species in terms of South Africa's National Environmental Management: Biodiversity Act (NEM:BA) of 2004. Despite regulations, illegal plant harvesting for medicinal trade has continued in South Africa and resulted in declines in cycad populations and even complete loss of sub-populations. Encephalartos is traded at traditional medicine markets in South Africa in the form of bark strips and stem sections; thus, determining the species traded presents a major challenge due to a lack of characteristic plant parts. Here, a case study is presented on the use of DNA barcoding to identify cycads sold at the Faraday and Warwick traditional medicine markets in Johannesburg and Durban, respectively. Market samples were sequenced for the core DNA barcodes (rbcLa and matK) as well as two additional regions: nrITS and trnH-psbA. The barcoding database for cycads at the University of Johannesburg was utilized to assign query samples to known species. Three approaches were followed: tree-based, similarity-based, and character-based (BRONX) methods. Market samples identified were Encephalartos ferox (Near Threatened), Encephalartos lebomboensis (Endangered), Encephalartos natalensis (Near Threatened), Encephalartos senticosus (Vulnerable), and Encephalartos villosus (Least Concern). Results from this study are crucial for making appropriate assessments and decisions on how to manage these markets.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".