Modeling the Asian Longhorned Tick (Acari: Ixodidae) Suitable Habitat in North America
Bibliographic record
Abstract
Asian longhorned or bush tick (Haemaphysalis longicornis Neumann) is a vector species of considerable medical and veterinary importance within its native range in East Asia, and in introduced areas of Australia and Oceania. Recently, this tick species was detected in several regions of the United States. This study aimed at modeling areas suitable for H. longicornis in North America using maximum entropy distribution modeling or Maxent. Occurrence records of H. longicornis within its present range were obtained from published literature. The Maxent model contained a small number of a priori climatic and ecological variables. Annual temperature, precipitation, and ecological zones were found the most important in creating sensitive and specific model (success rate = 91.8%) that had a good fit to the existing data. The model predicted suitable H. longicornis habitat in most of eastern North America from southern Canada to the Gulf Coast, and in a small temperate area on the West Coast. Coastal areas were among the highest ranked suitable habitat. Another highly suitable region was identified in mid-western and southern United States where Heartland virus transmission takes place. This finding is of concern, since H. longicornis ticks vector a closely related virus in East Asia. Delineation of areas suitable for H. longicornis can facilitate detection, prepare public health authorities, and inform the general public about this potentially pestiferous and medically important species.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".