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Record W4318612092 · doi:10.3390/zoonoticdis3010004

Ecology and Epidemiology of Lyme Disease in Western North America

2023· article· en· W4318612092 on OpenAlexaffabout
Carl Dizon, Tim Lysyk, Isabelle Couloigner, Susan C. Cork

Bibliographic record

VenueZoonotic Diseases · 2023
Typearticle
Languageen
FieldImmunology and Microbiology
TopicVector-borne infectious diseases
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsLyme diseaseEnzooticBorrelia burgdorferiTickEcologyVector (molecular biology)BiologyGeographyBiological dispersalIxodesVirologyEnvironmental healthPopulationMedicineImmunology

Abstract

fetched live from OpenAlex

Lyme disease is the most common vector-borne disease in the United States and Canada. The causative agent of Lyme disease in North America is the spirochete Borrelia burgdorferi. In western North America, the primary vector of Borrelia burgdorferi is the western black-legged tick, Ixodes pacificus. Surveillance and modelling efforts indicate that I. pacificus is primarily found in coastal California, Oregon, Washington and the southern coastal regions of British Columbia However, infection rates with B. burgdorferi among I. pacificus ticks remain low, ranging from 0.6% to 9.9%. Lyme disease case numbers in western North America are also relatively low compared to eastern North America. Enzootic maintenance of B. burgdorferi by hosts in natural environments and climatic factors may influence Lyme disease risk. The borreliacidal western fence lizard, Sceloporus occidentalis, may contribute to the low infection rates observed in I. pacificus ticks, while the migratory nature of avian hosts can allow for long-distance tick dispersal. Moderately warm and moist environments and protection from sunlight define the suitable habitats of I. pacificus ticks. In this review, we discuss the ecology and epidemiology of Lyme disease in relation to I. pacificus, as well as the need for more studies in western North America.

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)0.000
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 score0.004
Threshold uncertainty score0.906

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.017
GPT teacher head0.262
Teacher spread0.245 · 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

Citations5
Published2023
Admission routes2
Has abstractyes

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