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Record W1972946389 · doi:10.3389/fcimb.2013.00046

Changing geographic ranges of ticks and tick-borne pathogens: drivers, mechanisms and consequences for pathogen diversity

2013· review· en· W1972946389 on OpenAlexaff
Nicholas H. Ogden, Samir Mechai, Gabriele Margos

Bibliographic record

VenueFrontiers in Cellular and Infection Microbiology · 2013
Typereview
Languageen
FieldImmunology and Microbiology
TopicVector-borne infectious diseases
Canadian institutionsUniversité de MontréalCegep de Saint HyacinthePublic Health Agency of Canada
Fundersnot available
KeywordsTickBiologyBiological dispersalTick-borne diseaseEcologyLyme diseaseRange (aeronautics)IxodesIxodes scapularisPopulationPhylogeographyIxodidaeZoologyVirologyPhylogeneticsEnvironmental healthGenetics

Abstract

fetched live from OpenAlex

The geographic ranges of ticks and tick-borne pathogens are changing due to global and local environmental (including climatic) changes. In this review we explore current knowledge of the drivers for changes in the ranges of ticks and tick-borne pathogen species and strains via effects on their basic reproduction number (R 0), and the mechanisms of dispersal that allow ticks and tick-borne pathogens to invade suitable environments. Using the expanding geographic distribution of the vectors and agent of Lyme disease as an example we then investigate what could be expected of the diversity of tick-borne pathogens during the process of range expansion, and compare this with what is currently being observed. Lastly we explore how historic population and range expansions and contractions could be reflected in the phylogeography of ticks and tick-borne pathogens seen in recent years, and conclude that combined study of currently changing tick and tick-borne pathogen ranges and diversity, with phylogeographic analysis, may help us better predict future patterns of invasion and diversity.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.016
GPT teacher head0.229
Teacher spread0.214 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations181
Published2013
Admission routes1
Has abstractyes

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