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Record W3014499609 · doi:10.1007/s00300-020-02657-8

Polar bear denning distribution in the Canadian Arctic

2020· article· en· W3014499609 on OpenAlexaffabout
Katie R. N. Florko, Andrew E. Derocher, C-Jae C. Breiter, Maha Ghazal, Daryll Hedman, Jeff W. Higdon, Evan S. Richardson, Vicki Sahanatien, Vicki Trim, Stephen D. Petersen

Bibliographic record

VenuePolar Biology · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsNunavut Research InstituteAgriculture Food and Rural DevelopmentEnvironment and Climate Change CanadaUniversity of ManitobaUniversity of AlbertaUniversity of British Columbia
FundersWorld Wildlife Fund
KeywordsUrsus maritimusArcticHabitatSea iceDistribution (mathematics)The arcticClimate changeEcologyBiologyGeographyOceanography

Abstract

fetched live from OpenAlex

Abstract Declines in Arctic sea ice associated with climate change have resulted in habitat loss for ice-adapted species, while facilitating increased human development at higher latitudes. Development increases land-use and shipping traffic, which can threaten ecologically and culturally important species. Female polar bears ( Ursus maritimus ) and cubs are susceptible to disturbance during denning; a better understanding of denning habitat distribution may aid management. We compiled existing location data on polar bear denning ( n = 64 sources) in Canada between 1967 and 2018, including traditional ecological knowledge (TEK) studies, government and consultant reports, peer-reviewed scientific articles, and unpublished data acquired through data-sharing agreements. We synthesized these data to create a map of known denning locations. Most coastal regions in northern Canada supported denning, but large areas exist where denning is unreported. Gaps remain in the knowledge of polar bear denning in Canada and filling these will aid the conservation and management of polar bears in a changing Arctic.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.262
Threshold uncertainty score0.769

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.001

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 head0.024
GPT teacher head0.239
Teacher spread0.216 · 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.

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".

Quick stats

Citations17
Published2020
Admission routes2
Has abstractyes

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