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Record W2886555123 · doi:10.1111/acv.12439

Assessment of global polar bear abundance and vulnerability

2018· article· en· W2886555123 on OpenAlexafffund
Stephen Hamilton, Andrew E. Derocher

Bibliographic record

VenueAnimal Conservation · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaEnvironment and Climate Change CanadaArcticNetQuark ExpeditionsWorld Wildlife Fund
KeywordsUrsus maritimusArcticAbundance (ecology)Range (aeronautics)Sea icePopulationCircumpolar starGeographyLatitudeEcologyPhysical geographyClimate changeHabitatContinental shelfOceanographyBiologyFisheryGeologyDemography

Abstract

fetched live from OpenAlex

Abstract Estimates of abundance and trend are central to assessing population status; yet, are often challenging to obtain or unavailable, suffer from wide confidence intervals and may be collected at irregular intervals. Polar bearsUrsus maritimushave become an iconic species for climate change, yet information on abundance and status for significant parts of their range is unknown. We examine the existing information on subpopulation abundance of polar bears across their range to assess past monitoring. We model the relationship between subpopulation densities and ecological parameters including latitude, continental shelf habitat, prey diversity, sea ice extent and the length of the ice‐free season. Of the 19 subpopulations across the circumpolar Arctic, 14 have estimates (range: 161–2826 bears). Excluding three subpopulations that were regularly monitored, the mean interval between consecutive estimates was 10.9 years (range: 1–36 years), with only six subpopulations having estimates <10 years old. Subpopulation density estimates ranged from 0.57 to 9.30 bears per km2with a mean of 2.36 bears per 1000 km2and a median of 1.71 bears per 1000 km2. Our regression analysis found prey diversity as the only significant correlate with polar bear density. Based on this relationship, we estimate the global population at 23 315 bears (range: 15 972–31 212). An assessment of each subpopulation's vulnerability to climate change based on subpopulation size, amount of continental shelf habitat, prey diversity and changing ice conditions indicates that the Southern Beaufort Sea, Northern Beaufort Sea and Arctic Basin subpopulations are the most vulnerable followed by the Laptev Sea and Viscount Melville Sound subpopulations. With ongoing Arctic warming and the deleterious effects of sea ice loss on polar bears, we recommend that subpopulation assessments be conducted with greater frequency and in subpopulations lacking abundance estimates such that meaningful subpopulation monitoring can proceed.

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)0.001
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 score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
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 head0.023
GPT teacher head0.299
Teacher spread0.277 · 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".

Quick stats

Citations34
Published2018
Admission routes2
Has abstractyes

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