DNA barcodes identify medically important tick species in Canada
Bibliographic record
Abstract
Medically important ticks (Acari: Ixodidae) are often difficult to identify morphologically. A standardized, molecular approach using a 658 base pair DNA barcode sequence (from the 5' region of the mitochondrial cytochrome c oxidase subunit I gene) was evaluated for its effectiveness in discriminating ticks in North America, with an emphasis on Canadian ticks. DNA barcodes were generated for 96 of 154 specimens representing 26 ixodid species. A genetic cluster analysis was performed on the barcode sequences, which separated specimens into haplogroups closely corresponding with morphologically identified species. The tree topology was further supported by a BIN analysis. COI sequences generated were found to have a mean maximum intraspecific divergence of 1.59% and a mean nearest neighbour divergence of 12.8%, indicating a significant "barcode gap". This study also revealed possible cryptic diversity among specimens morphologically identified as Ixodes soricis and Ixodes texanus. A PCR-based test for Borrelia burgdorferi determined that 18.1% of Lyme-competent ticks in this study were positive. This study is also the first to record a B. burgdorferi-positive exoskeleton. In conclusion, DNA barcoding is a powerful tool that clinicians can use to determine the identification of tick specimens which can help them to suggest whether an attached tick is a potential health risk.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".