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Record W3024281754 · doi:10.3389/fvets.2020.00233

A Review of Pathogens, Diseases, and Contaminants of Muskrats (Ondatra zibethicus) in North America

2020· review· en· W3024281754 on OpenAlexaboutno aff
Laken S. Ganoe, Justin D. Brown, Michael J. Yabsley, Matthew J. Lovallo, W. David Walter

Bibliographic record

VenueFrontiers in Veterinary Science · 2020
Typereview
Languageen
FieldEnvironmental Science
TopicEnvironmental DNA in Biodiversity Studies
Canadian institutionsnot available
FundersPennsylvania Game Commission
KeywordsDiseaseBiologyPopulationEnvironmental healthEcologyGeographyMedicinePathology

Abstract

fetched live from OpenAlex

) harvest declines have been observed throughout North America. Several theories for the decline have been proposed, including increased parasite infections and disease within muskrat populations. No existing wholistic review of muskrat exposure to pathogens, contaminants, and diseases exists. To address this knowledge gap, we conducted a thorough review of existing literature on muskrat pathogens, contaminants, and diseases across their natural range. This review is comprised of 131 articles from 1915 to 2019 and from 27 U.S. states and 9 Canadian provinces. A wide diversity of contaminants, toxins, and pathogens were reported in muskrats, with the most common diseases being cysticercosis, tularemia, Tyzzer's disease, and biotoxin poisoning from cyanobacteria. This review provides a summary of muskrat pathogens, contaminants, and diseases over a century that has observed significant population declines throughout the species' range in North America. Such data provide a baseline for understanding the potential role of disease in these declines. In addition, these data highlight critical knowledge gaps that warrant future research efforts.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.829
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.002
Science and technology studies0.0000.004
Scholarly communication0.0000.000
Open science0.0010.002
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 head0.026
GPT teacher head0.269
Teacher spread0.243 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations22
Published2020
Admission routes1
Has abstractyes

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