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Record W2122013971 · doi:10.4039/n08-cpa04

Lyme borreliosis in Canada: biological diversity and diagnostic complexity from an entomological perspective

2009· article· en· W2122013971 on OpenAlexaffabout
Janet L. H. Sperling, Felix A. H. Sperling

Bibliographic record

VenueThe Canadian Entomologist · 2009
Typearticle
Languageen
FieldImmunology and Microbiology
TopicVector-borne infectious diseases
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsBorrelia burgdorferiIxodidaeLyme diseaseBiologyLyme borreliosisIxodesBorreliaTickDiversity (politics)ZoologyEcologyVirologyImmunologyAntibodyPolitical science

Abstract

fetched live from OpenAlex

Abstract Lyme borreliosis (LB), also known as Lyme disease, is emerging as a serious tickborne illness across Canada. More than three decades of research on LB in North America and Europe have provided a large, complex body of research involving well-documented difficulties at several levels. However, entomologists are well situated to contribute to resolving some of these challenges. The central pathogen in LB, the spirochete Borrelia burgdorferi Johnson et al. , includes numerous genospecies and strains that are associated with different disease symptoms and distributions. The primary vectors of LB are ticks of various Ixodes Latreille species (Acari: Ixodida: Ixodidae), but questions linger concerning the status of a number of other arthropods that may be infected with B. burgdorferi but do not transmit it biologically. A variety of vertebrates may serve as reservoirs for LB, but differences in their ability to transmit LB are not well understood at the community level. Persistent cystic forms of and immune system evasion by B. burgdorferi contribute to extraordinary challenges in diagnosing LB. Multiple trade-offs constrain the effectiveness of assays like ELISA, Western blot, polymerase chain reaction, and microscopic visualization of the spirochetes. Consequently, opportunities abound for entomologists to contribute to documenting the diversity of the players and their interactions in this devilishly complex disease.

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 score0.005
Threshold uncertainty score0.800

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.038
GPT teacher head0.249
Teacher spread0.211 · 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

Citations19
Published2009
Admission routes2
Has abstractyes

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