The hitchhiker's guide to becoming invasive: exotic mosquitoes spread across a <scp>US</scp> state by human transport not autonomous flight
Bibliographic record
Abstract
Not all exotic species establish and expand aggressively (i.e. become invasive). As potential vectors of disease agents, invasive mosquitoes can have considerable impact on public health, livestock and wildlife; therefore, understanding the species characteristics and ecological circumstances promoting their invasiveness is important. The mosquito Aedes japonicus japonicus, originally from north-east Asia, was introduced at least two separate times to the north-eastern USA, as surmised from the initial existence of two populations with distinct nuclear and mitochondrial genetic signatures that later intermixed. Since these original introductions in the late 1990s, Ae. j. japonicus has expanded across 31 US states, two Canadian provinces and five European countries. Although some of the expanded range was due to other independent introductions, to understand what drove the postintroduction expansion of Ae. j. japonicus within the north-eastern USA, we performed a high-resolution landscape genetic analysis of 461 specimens collected across Virginia, a state south of the original introductions. All specimens were genotyped at seven pre-optimized microsatellite loci, and a subsample was sequenced at one mitochondrial locus. We concluded that throughout Virginia this species has primarily expanded in association with humans: genetic distance and distance along roads remained correlated after controlling for geographic distance, and proximity to I-95, a major interstate highway, strongly predicted nuclear ancestry. In contrast, there was very limited evidence of diffusion even at distances potentially suitable for autonomous mosquito flight. This implies that its association with humans (rather than innate species characteristics) is the single most important determinant of invasiveness in this mosquito.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.044 | 0.025 |
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".