An analysis of the gaps in the South African DNA barcoding library of ticks of veterinary and public health importance
Bibliographic record
Abstract
Ticks transmit pathogens of veterinary and public health importance. Understanding their diversity is critical as infestations lead to significant economic losses globally. To date, over 90 species across three families have been identified in South Africa. However, the taxonomy of most species has not been resolved due to morphological identification challenges. DNA barcoding through the Barcode of Life Data Systems (BOLD) is therefore a valuable tool for species verifications for biodiversity assessments. This study conducted an analysis of South African tick COI barcodes on BOLD by verifying species on checklists, literature, and other sequence databases. The compiled list represented 97 species, including indigenous (59), endemics (27), introduced (2), invasives (1), and eight that could not be classified. Analyses indicated that 31 species (32%) from 11 genera have verified COI barcodes. These are distributed across all nine provinces with the Eastern Cape having the highest species diversity, followed by Limpopo, with KwaZulu-Natal having the least diversity. Rhipicephalus, Hyalomma, and Argas species had multiple barcode index numbers, suggesting cryptic diversity or unresolved taxonomy. We identified 21 species of veterinary or zoonotic importance from the Argasidae and Ixodidae families that should be prioritised for barcoding. Coordinating studies and defining barcoding targets is necessary to ensure that tick checklists are updated to support decision-making for the control of vector-borne diseases and alien invasives.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".