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Record W4205931101 · doi:10.1139/facets-2021-0027

Canadian wildlife health surveillance—patterns, challenges and opportunities identified by a scoping review

2022· review· en· W4205931101 on OpenAlexaffvenueabout
Jolene A. Giacinti, E. Jane Parmley, Mark Reist, Daniel Bayley, David L. Pearl, Claire M. Jardine

Bibliographic record

VenueFACETS · 2022
Typereview
Languageen
FieldMedicine
TopicZoonotic diseases and public health
Canadian institutionsHealth CanadaUniversity of Guelph
Fundersnot available
KeywordsWildlifeRepresentativeness heuristicOne HealthContext (archaeology)Environmental planningWildlife diseaseEnvironmental resource managementWildlife conservationGeographyBusinessEnvironmental healthPublic healthMedicineEcologyPsychology

Abstract

fetched live from OpenAlex

The protection and promotion of healthy wildlife populations is emerging as a shared goal among stakeholders in the face of unprecedented environmental threats. Accordingly, there are growing demands for the generation of actionable wildlife health information. Wildlife health surveillance is a connected system of knowledge that generates data on a range of factors that influence health. Canada recently approved the Pan-Canadian Approach to Wildlife Health that describes challenges facing wildlife health programs and provides a path forward for modernizing our approach. This scoping review was undertaken to describe the range of peer-reviewed Canadian wildlife health surveillance literature within the context of the challenges facing wildlife health programs and to provide a quantitative synthesis of evidence to establish baselines, identify gaps, and inform areas for growth. This review describes patterns related to species, location, authorship/funding, objectives, and methodology. Five areas are identified that have the potential to propel the field of wildlife health: representativeness, expanded/diversified collaboration, community engagement, harmonization, and a shift to a solutions-focused and One Health mindset. This scoping review provides a synopsis of 10 years of Canadian wildlife health surveillance, challenges us to envision the future of successful wildlife health surveillance, and provides a benchmark from which we can measure change.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.678
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
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)0.0020.000

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 head0.237
GPT teacher head0.410
Teacher spread0.172 · 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

Citations10
Published2022
Admission routes3
Has abstractyes

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