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Record W2904780928 · doi:10.1093/jme/tjy210

Modeling the Asian Longhorned Tick (Acari: Ixodidae) Suitable Habitat in North America

2018· article· en· W2904780928 on OpenAlexaboutno aff
Ilia Rochlin

Bibliographic record

VenueJournal of Medical Entomology · 2018
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Vectors
Canadian institutionsnot available
Fundersnot available
KeywordsHaemaphysalis longicornisTickHabitatEcologyRange (aeronautics)IxodidaeBiologyAcariEast AsiaTemperate climateGeographyChinaArchaeology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.350
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.024
GPT teacher head0.321
Teacher spread0.296 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations90
Published2018
Admission routes1
Has abstractyes

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