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Record W3088060570 · doi:10.11575/prism/38223

Health Surveillance of Thinhorn Sheep (Ovis dalli) Herds in British Columbia and Alaska

2019· dissertation· en· W3088060570 on OpenAlexaboutno aff
C. K. Thacker

Bibliographic record

VenueOpen MIND · 2019
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Disease Management and Epidemiology
Canadian institutionsnot available
FundersFoundation for North American Wild Sheep
KeywordsOvisGeographyOvis canadensisFisheryEcologyBiologyMedicineEnvironmental healthPopulation

Abstract

fetched live from OpenAlex

The health of wildlife populations influences their sustainability in the face of ecological challenges. There is a paucity of information about the health status of free ranging thinhorn sheep populations (Ovis dalli), despite their economic, ecological, and cultural significance. Identification of health concerns in related species, bighorn sheep (Ovis canadensis), as well as concern from local communities, First Nations, hunters, and conservationists that thinhorn subpopulations may be declining in some areas, prompted the call for comprehensive thinhorn sheep herd health assessments. We used a standardized approach based on similar work on bighorn herd health and conducted herd health assessments of thinhorn sheep in five study herds across their range that included both subspecies, Dall’s sheep (O. dalli dalli) and Stone’s sheep (O. dalli stonei). We used a broad definition of health and surveyed exposure to multiple pathogens common to domestic small ruminants and other wildlife species, and evaluated other comprehensive health measures including nutritional status, parasite burden, contaminant exposure, stress, pregnancy, and indices of body condition. From 2017 to 2020 we collected tissue and blood samples from 46 Stone’s sheep ewes and immature rams live-captured in the Skeena and Peace regions of British Columbia (BC), and 67 Dall’s sheep in the Talkeetna and Chugach mountains of Alaska (AK). We also analyzed samples from 63 hunter-harvested Stone’s sheep rams from the Skeena region of BC from 2016 to 2019. We found evidence of Mycoplasma ovipneumoniae exposure in Dall’s sheep in Alaska and inconclusive results in Stone’s sheep in BC. There was minimal evidence of exposure to other bacterial and viral respiratory pathogens in all subspecies and herds. A high seroprevalence to ovine herpesvirus (P = 89.5%) was detected in all Stone’s sheep. Parasite burdens were similar to previously reported results, including winter tick (Dermacentor albipictus) infestations of Stone’s sheep sampled at low elevation along the Peace Arm of Williston reservoir. A high seroprevalence to Toxoplasma gondii was detected in sheep in Alaska (P = 100% in 2019, and 73.9% in 2020). Fecal glucocorticoid metabolite concentrations determined from hunter-harvested and live-captured sheep increased annually. Serum and tissue copper levels in some herds were in the range considered deficient for domestic sheep. Other trace minerals, including zinc and selenium, were deficient only in some study areas. Body condition of hunter-harvested rams decreased annually from 2016 to 2018. Our findings confirm that thinhorn sheep, in general, are relatively naïve, and in some populations, very naïve, to diseases carried by domestic ruminants and other wildlife species. This information provides a baseline for thinhorn sheep herd health monitoring. If continued, it will allow for early detection of disease introductions and other population-limiting health factors. The results inform conservation and One Health decision-making and can be incorporated into science-based management of thinhorn sheep in BC and Alaska.

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)0.001
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 score0.194
Threshold uncertainty score0.391

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.029
GPT teacher head0.288
Teacher spread0.259 · 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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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