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Record W7163617372

Exploring relationships between winter tick (Dermacentor albipictus) ecology, moose (Alces alces), and climate in Jackson Hole, Wyoming

2024· dissertation· en· W7163617372 on OpenAlexaboutno aff
Troy Matthew Koser

Bibliographic record

VenueMontana State University ScholarWorks (Montana State University) · 2024
Typedissertation
Languageen
FieldImmunology and Microbiology
TopicVector-borne infectious diseases
Canadian institutionsnot available
Fundersnot available
KeywordsTickAbundance (ecology)Range (aeronautics)Tick infestationClimate changeOccupancy
DOInot available

Abstract

fetched live from OpenAlex

Global ecosystem changes are affecting how parasites and their hosts interact. Winter ticks (Dermacentor albipictus) are ectoparasites of moose (Alces alces) which can cause anemia, hair loss, and even death due to large numbers of hundreds to thousands of ticks feeding. Warming temperatures are expected to increase winter tick abundance and activity windows while habitat loss and other factors diminish moose nutritional conditions making them more susceptible to complications from tick infestations. Though some of these factors are well- studied in parts of Canada and the northeastern U.S., winter tick, moose, and climate interactions are not well understood in the western U.S. We surveyed winter ticks, moose, and elk in Jackson Hole, Wyoming from 2020-2023 and found elk to be feasible reservoirs for winter ticks, potentially impacting moose infestation rates. In 2021 we deployed scent detection dogs to find winter ticks in the field and determined that they are indeed capable of surveying for winter ticks but do not dramatically outperform the traditional tick drag survey. We found that average maximum temperature and vapor pressure deficit over summer affect winter tick occupancy rates but we did not find climate associations with winter tick abundance over three years of surveys in Jackson Hole. We did, however, find temperature to affect winter tick survival and reproduction in monitoring surveys, implying a potential mismatch between extrapolated climate datasets and conditions experienced by ticks. Finally, we found moose that spend more of their fall home range in 'urban' versus 'rural' landscapes and with higher spring and fall seasonal home range overlaps to be in higher hair loss categories. Future research in winter tick-moose systems, and in tick-borne disease research as a whole, should take into account host movement patterns and the potential impacts of multiple competent host species on the landscape.

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 score0.478
Threshold uncertainty score0.951

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.031
GPT teacher head0.223
Teacher spread0.191 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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