{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001056762,0.00009564929,0.0001365013,0.00000619917,0.0001978635,0.00003194193,0.00009745443,0.00004043448,0.004034589],"category_scores_gemma":[0.00002906695,0.0000893125,0.00002765078,0.0001639713,0.0001581664,0.0001775141,0.0003641594,0.0000521356,0.00005137091],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005097633,"about_ca_system_score_gemma":0.000006994268,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006945317,"about_ca_topic_score_gemma":0.01756994,"domain_scores_codex":[0.99927,0.00003201846,0.0001766633,0.000192377,0.0001610707,0.0001678249],"domain_scores_gemma":[0.9996812,0.0000139924,0.00009292184,0.0001499746,0.00001173958,0.00005012928],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000003240457,0.00002055614,0.9559606,0.00001846609,0.000008782875,0.000001128597,0.00008420319,0.00006248611,0.002034754,0.00003680998,0.0003835924,0.04138542],"study_design_scores_gemma":[0.0001021343,0.00005644092,0.9912149,0.00002007152,0.00001986034,0.000002963641,0.0001534695,0.004409088,0.0009494519,0.0002141134,0.002741168,0.0001163602],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9774512,0.0002535625,0.001608384,0.00003377851,0.00009058407,0.00009038911,0.000006314823,0.00001108151,0.02045469],"genre_scores_gemma":[0.9772964,0.0000273853,0.0224593,0.00004256659,0.00009986355,9.468214e-7,0.000002188533,0.00001275672,0.00005862191],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04126906,"threshold_uncertainty_score":0.9996675,"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."}}