{"id":"W4411932007","doi":"10.1016/j.isci.2025.112870","title":"Population-genomics reveals a dual ancestry of grizzly bears","year":2025,"lang":"en","type":"article","venue":"iScience","topic":"Genetic diversity and population structure","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Moncton; Environment and Climate Change Canada","funders":"Canada Research Chairs; Université de Moncton; Leibniz-Gemeinschaft; Government of Nunavut; Alaska Department of Fish and Game","keywords":"Genomics; Population genomics; Population; Dual (grammatical number); Biology; Evolutionary biology; Grizzly Bears; Computational biology; Geography; Data science; Genetics; Genome; Computer science; Demography; Sociology; Gene; Philosophy","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000219422,0.00009303896,0.0001427273,0.0003227041,0.0002806615,0.0003106582,0.0001363385,0.0001825251,0.0005590655],"category_scores_gemma":[0.0002611253,0.0000726573,0.0001174015,0.0002683858,0.0003466982,0.0002806131,0.0003089457,0.0002982904,0.00007798072],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001433222,"about_ca_system_score_gemma":0.0000946507,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001489906,"about_ca_topic_score_gemma":0.003160033,"domain_scores_codex":[0.9999169,0.00001200247,0.000002730716,0.00004448344,0.00001190846,0.00001191358],"domain_scores_gemma":[0.9998666,0.00003299679,0.00005334167,0.00001749772,0.0000126038,0.00001686287],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003342938,0.00006584294,0.6105731,0.0000553979,0.0002154842,0.0002846237,0.002129241,0.001528003,0.3558272,0.003646237,0.0002387207,0.02510187],"study_design_scores_gemma":[0.00000414825,0.00005109225,0.9940461,0.000004714936,0.00003782368,0.0001758164,0.00041115,0.001245902,0.002297421,0.001174528,0.0005446182,0.000006549744],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9993663,0.00004768261,0.0003170925,0.00001974529,7.421363e-7,3.895774e-7,0.00003708345,0.000003433741,0.0002075048],"genre_scores_gemma":[0.9994059,0.00005321473,0.0003357517,0.0000159097,0.000002943134,8.850798e-7,0.00009281441,0.000002237656,0.00009045994],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001489906,"threshold_uncertainty_score":0.00296241,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01607719410490212,"score_gpt":0.2680111824572699,"score_spread":0.2519339883523678,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}