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Record W2098238814 · doi:10.1111/mec.13038

Genomic evidence of geographically widespread effect of gene flow from polar bears into brown bears

2014· article· en· W2098238814 on OpenAlexaff
James A. Cahill, Ian Stirling, Logan Kistler, Rauf Salamzade, Erik Ersmark, Tara L. Fulton, Mathias Stiller, Richard E. Green, Beth Shapiro

Bibliographic record

VenueMolecular Ecology · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Phylogenetic Studies
Canadian institutionsAlberta Environment and Protected Areas
FundersSearle Scholars ProgramGordon and Betty Moore FoundationDavid and Lucile Packard FoundationNational Science Foundation
KeywordsBiologyGene flowArcticGenomePopulationMainlandEvolutionary biologyEcologyGeneticsGeneGenetic variationDemography

Abstract

fetched live from OpenAlex

Polar bears are an arctic, marine adapted species that is closely related to brown bears. Genome analyses have shown that polar bears are distinct and genetically homogeneous in comparison to brown bears. However, these analyses have also revealed a remarkable episode of polar bear gene flow into the population of brown bears that colonized the Admiralty, Baranof and Chichagof islands (ABC islands) of Alaska. Here, we present an analysis of data from a large panel of polar bear and brown bear genomes that includes brown bears from the ABC islands, the Alaskan mainland and Europe. Our results provide clear evidence that gene flow between the two species had a geographically wide impact, with polar bear DNA found within the genomes of brown bears living both on the ABC islands and in the Alaskan mainland. Intriguingly, while brown bear genomes contain up to 8.8% polar bear ancestry, polar bear genomes appear to be devoid of brown bear ancestry, suggesting the presence of a barrier to gene flow in that direction.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.073
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.004
GPT teacher head0.218
Teacher spread0.213 · 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 designBench or experimental
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

Citations176
Published2014
Admission routes1
Has abstractyes

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