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Record W3103579766 · doi:10.3390/vetsci7040177

Prevalence of Winter Ticks (Dermacentor albipictus) in Hunter-Harvested Wild Elk (Cervus canadensis) from Pennsylvania, USA (2017–2018)

2020· article· en· W3103579766 on OpenAlexaboutno aff
Elizabeth Calvente, Samantha Pelletier, Jeremiah E. Banfield, Justin D. Brown, Nicole Chinnici

Bibliographic record

VenueVeterinary Sciences · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsnot available
Fundersnot available
KeywordsCervusBiologyDermacentor variabilisZoologyEcologyGeographyTickIxodidae

Abstract

fetched live from OpenAlex

Winter ticks (Dermacentor albipictus) are an aggressive one-host tick that infest a wide-diversity of ungulates. Infestations can result in anemia, alopecia, emaciation, and death. Most notably, the winter tick has caused negative impacts to moose (Alces alces) populations in the northeast United States and Canada. Winter ticks have been identified on other cervid species, including deer (Odocoileus virginianus) and elk (Cervus canadensis), which generally results in low tick burdens and mild or no disease. Recently, however, a wild yearling bull elk in Pennsylvania was found dead as a result of severe winter tick infestation. To obtain baseline data on winter ticks in wild elk in Pennsylvania, we collected 1453 ticks from 190 hunter-harvested wild elk between 2017–2018. Of the 204 harvested elk, 94.3% (190/204) had ticks collected for this study and none of the sampled elk had evidence of winter-tick associated disease. The average tick burden was 7.7 ticks/elk and average winter tick load on all elk was 0.5. Results of this study indicate that winter ticks do infest wild elk in Pennsylvania. However, during the fall months, the tick burden is low and rarely associated with lesions. These data herein serve as a baseline to monitor winter tick populations over time.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.023
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

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

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 head0.048
GPT teacher head0.258
Teacher spread0.210 · 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 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

Citations11
Published2020
Admission routes1
Has abstractyes

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