{"id":"W2887069798","doi":"10.1002/ecs2.2364","title":"Assessing spatial discreteness of Hudson Bay polar bear populations using telemetry and genetics","year":2018,"lang":"en","type":"article","venue":"Ecosphere","topic":"Marine animal studies overview","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Trent University; Ministry of Natural Resources and Forestry; Environment and Climate Change Canada; Government of Nunavut; Alberta Environment and Protected Areas; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Churchill Northern Studies Centre; Alberta Innovates - Technology Futures; ArcticNet; Quark Expeditions; World Wildlife Fund","keywords":"Ursus maritimus; Population; Biology; Genetic diversity; Population genetics; Ursus; Wildlife management; Wildlife; Bay; Ecology; Conservation genetics; Genetic structure; Genetic monitoring; Evolutionary biology; Geography; Microsatellite; Genetics; Demography; Arctic","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.001028231,0.0001337789,0.00014755,0.001411716,0.0003830525,0.0005121573,0.0002660897,0.00009628118,0.0006459724],"category_scores_gemma":[0.001379834,0.0001119657,0.0001248208,0.0008546998,0.0005222427,0.0003283087,0.0004929615,0.000155036,0.00005462203],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000794459,"about_ca_system_score_gemma":0.0005186781,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1062462,"about_ca_topic_score_gemma":0.1956307,"domain_scores_codex":[0.9996554,0.00009669181,0.00002222584,0.0001003772,0.00008324424,0.00004194399],"domain_scores_gemma":[0.9988129,0.0002487447,0.0004460779,0.00009562202,0.0002819771,0.0001147311],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002336355,0.000006745961,0.9953722,0.00000508482,0.00002650204,0.00001222658,0.0001740165,0.0001613499,0.0007146255,0.00006212955,0.00004287984,0.003398842],"study_design_scores_gemma":[0.000001334271,0.00001459869,0.9990018,0.000005075691,0.000005812325,0.00001616354,0.0004621749,0.0002548074,0.0001128061,0.00002583284,0.00009847608,0.000001139405],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.999193,0.00009131725,0.000259535,0.00001709411,0.000002012098,0.000003046365,0.00009602997,0.000001982572,0.0003359917],"genre_scores_gemma":[0.9993086,0.00005759209,0.0003810304,0.000008348702,0.000003407371,0.000005168524,0.0001265144,6.772041e-7,0.0001085709],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8937538,"threshold_uncertainty_score":0.2112554,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04418685524261872,"score_gpt":0.3029095735719993,"score_spread":0.2587227183293806,"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."}}