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Record W4404310048 · doi:10.1139/as-2024-0056

Building the foundation for polar bear science: fifty years of research on polar bears in Western Hudson Bay

2024· article· en· W4404310048 on OpenAlexafffundvenueabout
Brooke A. Biddlecombe, Andrew E. Derocher, Elizabeth Krebs, Nicholas J. Lunn, David McGeachy, Evan S. Richardson

Bibliographic record

VenueArctic Science · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsUniversity of AlbertaEnvironment and Climate Change Canada
FundersGovernment of Canada
KeywordsBayPolarFoundation (evidence)Ursus maritimusGeographyArchaeologyPhysicsAstronomy

Abstract

fetched live from OpenAlex

In 1966, Environment and Climate Change Canada (ECCC) (then Canadian Wildlife Service) initiated a research program on polar bears ( Ursus maritimus, Phipps, 1774) belonging to the Western Hudson Bay subpopulation (WH). This paper provides an overview of that program, highlighting the long-term research on WH polar bears with a focus on ECCC-led work. The WH research program, which has now extended across five decades with data on over 4600 individual bears, has evolved from a study of the basic ecology of polar bears into foundational work on the life history, demography, genetics, movement, behaviour, and ecology of an apex predator in a rapidly changing Arctic. Research on polar bears in Canada supports commitments under the Agreement on the Conservation of Polar Bears (1973), the Convention on Biological Diversity (1992), and Canada’s Species at Risk Act (2002). Among Canada’s 13 polar bear subpopulations, only WH has sufficient long-term monitoring of individuals to assess demographic, behavioural, and life history consequences of climate change and other anthropogenic stressors. Future research should continue to ask key questions on how long-term environmental changes impact the ecology of polar bears. Integrating community priorities into the research program is necessary for it to continue to be successful in the future.

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)0.008
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 score0.332
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.004
Science and technology studies0.0010.003
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.065
GPT teacher head0.385
Teacher spread0.320 · 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

Citations3
Published2024
Admission routes4
Has abstractyes

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