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Record W4409718530 · doi:10.1139/as-2025-0021

Long-term wildlife research and monitoring sites in Arctic Canada

2025· article· en· W4409718530 on OpenAlexaffvenueabout
Amie L. Black, Jason A. Akearok, Mark L. Mallory

Bibliographic record

VenueArctic Science · 2025
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsAcadia UniversityNunavut Wildlife Management BoardEnvironment and Climate Change Canada
Fundersnot available
KeywordsWildlifeTerm (time)ArcticGeographyEnvironmental scienceEnvironmental resource managementEnvironmental planningEnvironmental protectionEcologyBiology

Abstract

fetched live from OpenAlex

In Arctic Canada, the development of long-term research sites to study vertebrates (principally marine birds and marine mammals) has supported the safe delivery of collaborative science that is unique in its ability to assess the impacts of rapid environmental change and increasing human-generated pressures (e.g., fisheries bycatch, harvest, tourism, resource development) to wildlife and their habitats. The longevity of these research programs has enabled them to be impactful in their ability to inform environmental and wildlife conservation and management actions in Canada and internationally. In this Special Issue, we have collected a series of papers describing key long-term (>10 years) Arctic wildlife research and monitoring sites (mainly in Nunavut), including why they were developed, their scientific accomplishments, their impact on domestic or international policy, the relevance of the research to local Inuit communities, and how they might evolve to address future conservation issues. In this overview paper, we describe the social, political, and scientific context underlying the development and delivery of science at these sites over the past 40 years, and comment on the role of these programs in supporting the evolution of wildlife research in Arctic Canada.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.198
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0050.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.110
GPT teacher head0.469
Teacher spread0.359 · 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 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
Published2025
Admission routes3
Has abstractyes

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