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Record W1531037104 · doi:10.1111/mms.12251

Aerial surveys suggest long‐term stability in the seasonally ice‐free Foxe Basin (Nunavut) polar bear population

2015· article· en· W1531037104 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueMarine Mammal Science · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsGovernment of Nunavut
FundersU.S. Geological SurveyUniversity of Minnesota
KeywordsTransectArcticAerial surveyGeographyPhysical geographyPopulationDistance samplingSea iceStructural basinEnvironmental scienceClimate changeAbundance (ecology)Population growthEcologyCartographyGeologyBiologyDemographyMeteorology

Abstract

fetched live from OpenAlex

Abstract Significant information gaps exist regarding the status of polar bears, especially with respect to the impacts of climate change, across large portions of the Arctic. To obtain an updated abundance estimate for the Foxe Basin population, we conducted comprehensive aerial surveys during the 2009 and 2010 ice‐free seasons, when bears are confined to land. We sampled with mark‐recapture distance sampling protocols on inland and coastal transects and surveyed small islands and remnant ice floes. We observed 816 and 1,003 bears in 2009 and 2010, respectively. Although detection functions differed substantially between years, estimates were consistent between analytical methods and years. Averaging four estimates (two from each year) yielded 2,585 (2,096–3,189) bears, which is similar to an estimate from the 1990s. This result, along with robust cub production, suggests a stable and healthy population despite deteriorating sea ice conditions. Collectively, this and other recent on‐land surveys provide a framework for implementing aerial surveys elsewhere. Although aerial surveys do not yield estimates of vital rates or population growth, they enable more rapid and frequent monitoring than mark‐recapture. Integrating them in long‐term monitoring programs will require consideration of ancillary data to infer status and facilitate setting harvest levels.

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.

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)0.009
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.243
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0020.004
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 head0.032
GPT teacher head0.257
Teacher spread0.224 · 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