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Record W2182461416 · doi:10.26443/msurj.v6i1.96

Assessment of human health risk for lyme disease in a peri-urban park in southern Québec

2011· article· en· W2182461416 on OpenAlexaffabout
Christina P. Tadiri, Nick Ainsworth, Nathaniel De Bono, Jun Li, Katherine Milbers, Lien Sardinas, Naomi Schwartz

Bibliographic record

VenueMcGill Science Undergraduate Research Journal · 2011
Typearticle
Languageen
FieldImmunology and Microbiology
TopicVector-borne infectious diseases
Canadian institutionsMcGill University
Fundersnot available
KeywordsLyme diseaseIxodes scapularisBorrelia burgdorferiTickGeographyRisk perceptionLYMEEnvironmental healthPublic healthDiseasePerceptionIxodidaeEcologyBiologyMedicineVirologyImmunology

Abstract

fetched live from OpenAlex


 
 
 Introduction: climate change has contributed to the spread of the hard tick Ixodes scapularis into increasingly northern latitudes, and subsequently has caused the spread of the lyme dis-ease causing bacterium, Borrelia burdorferi, into these northern areas. The spread of these ticks into the region of southern Québec is highly likely within the near future. As a result, new human populations are being exposed to these ticks and are at risk for contracting lyme disease. intent: This exploratory study examines the spatial and behavioral factors associated with human activity in longueuil regional park in relation to risk for lyme disease. Methods: we conducted exit surveys of park-goers to determine spatial and behavioral patterns of park use, as well as lyme disease awareness. results and conclusion: we found higher awareness of ticks in female park-goers, park-goers over 50, and high-frequency park-goers. our results, importantly, imply a discrepancy between peoples' awareness of tick bite precautions, and their perception of tick bite risk. we hope that these findings may help future research on the spread of lyme disease into Canada, as well as in the formulation of public health policy.
 
 

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)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.078
GPT teacher head0.384
Teacher spread0.307 · 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.

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

Citations2
Published2011
Admission routes2
Has abstractyes

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