{"id":"W4295511570","doi":"10.1371/journal.pone.0274189","title":"New strategies for characterizing genetic structure in wide-ranging, continuously distributed species: A Greater Sage-grouse case study","year":2022,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Rangeland and Wildlife Management","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Rocky Mountain Research Station; Natural Sciences and Engineering Research Council of Canada; Natural Resources Conservation Service; Nevada Department of Wildlife; U.S. Bureau of Land Management; California Department of Fish and Wildlife; Colorado Parks and Wildlife; U.S. Fish and Wildlife Service; Washington Department of Fish and Wildlife; Utah Division of Wildlife Resources; Great Northern Landscape Conservation Cooperative; U.S. Forest Service; U.S. Geological Survey","keywords":"Genetic structure; Gene flow; Range (aeronautics); Population; Habitat; Biology; Genetic diversity; Ecology; Conservation genetics; Isolation by distance; Evolutionary biology; Microsatellite; Geography; Genetics; Gene; Allele; Demography","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":[0.001635392,0.000308664,0.000257322,0.00137305,0.0005852911,0.0007060442,0.0005480797,0.0005318515,0.000482952],"category_scores_gemma":[0.002817391,0.0001303317,0.0003346711,0.001417883,0.0007308062,0.0006455336,0.0009242235,0.0005862117,0.00008884153],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004876893,"about_ca_system_score_gemma":0.0005457019,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01000396,"about_ca_topic_score_gemma":0.04836404,"domain_scores_codex":[0.9993101,0.0003307041,0.000032097,0.0001464156,0.0001480667,0.00003267517],"domain_scores_gemma":[0.9985771,0.0006010324,0.0002559296,0.0002031196,0.000233721,0.0001291762],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001535731,0.000283524,0.6960001,0.0002370572,0.000208307,0.00323716,0.009110959,0.01121398,0.06741847,0.008775973,0.001101422,0.2022594],"study_design_scores_gemma":[0.0000492518,0.0006898966,0.8570055,0.0001736616,0.0002287022,0.005806824,0.00825198,0.08683146,0.01544065,0.01357583,0.0117752,0.000170993],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9121804,0.0004167129,0.08320518,0.0003465169,0.0000116027,0.0001611668,0.0001630042,0.00007101344,0.003444383],"genre_scores_gemma":[0.7475818,0.0003427925,0.2507615,0.0001349782,0.00001740318,0.000110955,0.0002037628,0.00002051797,0.000826339],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01000396,"threshold_uncertainty_score":0.01989144,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02526154414599109,"score_gpt":0.206932575827281,"score_spread":0.1816710316812899,"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."}}