Integrated Approaches in Support of Taxonomic Identification of Mosquitoes (Diptera: Culicidae) in Vector Surveillance in Spain
Bibliographic record
Abstract
The pandemic of Zika virus in 2016 and other arboviruses prompted La Rioja Government in Spain to implement an entomological surveillance program of mosquitoes (Diptera; Culicidae) in the region of La Rioja. The morphological identification was supported by genetic analysis using the COI (cytochrome c oxidase subunit I) and the ITS2 (internal transcribed spacer 2) genes. In total, we identified 24 species arranged in 6 genera: Aedes (7 species), Anopheles (4 species), Coquillettidia (1 species), Culex (7 species), Culiseta (4 species), and Uranotaenia (1 species). Aedes sticticus and Aedes geniculatus are newly reported for La Rioja region. In total, 465 COI sequences were analyzed for Culicinae and Anophelinae and 54 ITS2 sequences for Anophelinae; all individuals identified as the same species clustered together in the Neighbor Joining trees. The levels of sequence divergence based on COI ranged between 0% and 2.62%, while the interspecific genetic divergence ranged from 3.05% to 20.07%. Within the genus Culiseta, certain specimens of Culiseta annulata , Culiseta litorea, and Culiseta subochrea were morphologically misidentified due to variation in the main diagnostic characters. The interspecific genetic divergence based on the ITS2 ranged from 0% to 2.98%. An accurate identification of mosquito vectors is the first step to establish a vector surveillance program for preventing pathogen transmission.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".