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Record W3135708432 · doi:10.1093/jme/tjab019

Distribution of <i>Dermacentor andersoni</i> (Acari: Ixodidae) in Grassland Regions of Alberta, Canada

2021· article· en· W3135708432 on OpenAlexaffabout
Tim Lysyk, Shaun J. Dergousoff, Kateryn Rochon, Neil B. Chilton, A. M. Smith

Bibliographic record

VenueJournal of Medical Entomology · 2021
Typearticle
Languageen
FieldImmunology and Microbiology
TopicVector-borne infectious diseases
Canadian institutionsUniversity of SaskatchewanUniversity of ManitobaAgriculture and Agri-Food Canada
Fundersnot available
KeywordsTickAcariIxodidaeBiologyGrasslandEcologyPopulation densityVeterinary medicinePhysical geographyGeographyPopulation

Abstract

fetched live from OpenAlex

The geographic distribution of the Rocky Mountain wood tick, Dermacentor andersoni Stiles, was determined in Alberta, Canada, by drag sampling at 86 and 89 sites during 2011 and 2012, respectively. Tick density and prevalence varied between years, averaging (range) 1.0 (0-26.2) and 5.9 (0-110) ticks/1,000 m2 in 2011 and 2012, respectively. Ticks were detected at 24.4% and 42.7% of the sites sampled in each respective year. Tick density and presence declined in a northerly direction to 51.6°N and in a westerly direction to ca. 113°W, except for a small area of high density at the edge of the Rocky Mountains in the southeastern portion of the province. Ticks were most abundant in the Dry Mixedgrass and Montane natural subregions and in areas with Brown Chernozemic, Regosol, and Solodized Solonetzic great soil groups. A logistic regression model indicated that tick presence was increased in the Dry Mixedgrass natural subregion and in regions with greater temperatures during the previous summer and normal winter precipitation but was reduced in areas with Dark Brown Chernozemic soils. The model will be useful for predicting tick presence and the associated risk of tick-borne diseases in the province.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.272
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.009
GPT teacher head0.247
Teacher spread0.238 · 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

Citations7
Published2021
Admission routes2
Has abstractyes

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