Comparison of Habitat Suitability Models for Haemaphysalis longicornis Neumann in North America to Determine Its Potential Geographic Range
Bibliographic record
Abstract
Haemaphysalis longicornis Neumann, 1901 is a vector of many pathogens of public and veterinary health importance in its native range in East Asia and introduced range in Oceania. In North America, this tick was first detected in New Jersey in 2017. Currently, this tick has been reported from 15 states of the United States. In this study, we modeled the habitat suitability of H. longicornis using the MaxEnt modeling approach. We separated occurrence records from the published literature from four different geographical regions in the world and developed MaxEnt models using relevant environmental variables to describe the potential habitat suitability of this tick in North America. The predictive accuracy of the models was assessed using the U.S. county locations where this tick species has been reported. Our best model predicted that the most suitable North American areas for geographic expansion of H. longicornis are from Arkansas–South Carolina to the south of Quebec–Nova Scotia in the east, and from California to the coast of British Columbia in the west. Enhanced surveillance and further investigation are required to gain a better understanding of the role that this tick might play in the transmission of diseases to humans and animals in North America.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".