A barcoding approach to phylogenetic classification of Aedini mosquitoes ( <i>Aedes, Ochlerotatus</i> )
Bibliographic record
Abstract
Abstract Traditionally, entomologists have used morphological characteristics for mosquito taxonomy and systematics. However, this approach does not take into consideration the genetic relatedness of species. In 2000, the Aedes genus of mosquitoes in the tribe Aedini was split into two genera ( Aedes and Ochlerotatus ), thereby elevating Ochlerotatus from subgenus to genus rank, strictly based on morphology of adults. Herein, we use the genetic barcoding marker COI to generate a phylogeny of 65 species of Aedes, Ochlerotatus , and Anopheles outgroup from almost 900 sequences downloaded from BOLD systems. Our results reveal evidence of non-random, but polyphyletic clustering of Aedes and Ochlerotatus species, with a monophyletic outgroup. We do find support for the validity of Ochlerotatus as an evolutionary unit, although we find insufficient evidence to support its retention as a genus. We suggest that mosquito phylogenetic analyses incorporate a greater number of genetic markers to help clarify our understanding of Aedini species classifications, but caution that recent assessments based solely on morphology may be insufficient.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".