{"id":"W2604804277","doi":"10.1093/molbev/msx110","title":"PopNet: A Markov Clustering Approach to Study Population Genetic Structure","year":2017,"lang":"en","type":"article","venue":"Molecular Biology and Evolution","topic":"Genetic Mapping and Diversity in Plants and Animals","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children; University of Toronto","funders":"Canadian Institutes of Health Research; National Institutes of Health","keywords":"Biology; Cluster analysis; Evolutionary biology; Population; Genome; Population genomics; Genomics; Genetics; Computational biology; Artificial intelligence; Computer science; Gene","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":[0.002067011,0.00114404,0.001248556,0.003047906,0.0015126,0.001283489,0.002633793,0.001305086,0.004411365],"category_scores_gemma":[0.007851204,0.0009061948,0.001814071,0.002042621,0.0008394627,0.001315945,0.001977478,0.001584128,0.0007963777],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001589126,"about_ca_system_score_gemma":0.002204575,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02012691,"about_ca_topic_score_gemma":0.02681066,"domain_scores_codex":[0.9993018,0.0003285968,0.00003736098,0.0001729912,0.0001110091,0.00004816328],"domain_scores_gemma":[0.9969401,0.00208102,0.0002336181,0.0002812595,0.0002813129,0.00018267],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001140252,0.00008120418,0.005916921,0.0001777235,0.0004440806,0.0002223487,0.0002939471,0.8944072,0.002099677,0.04606957,0.005087514,0.0450858],"study_design_scores_gemma":[0.00001319491,0.000009028712,0.0002744549,0.00000935404,0.00001282674,0.00002983079,0.00002474222,0.9730733,0.0002138542,0.02497674,0.001351427,0.00001117989],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01358287,0.0001669276,0.9821734,0.0001705477,0.00004088384,0.00008948616,0.0008094843,0.001810318,0.001156048],"genre_scores_gemma":[0.2097185,0.0003949988,0.7818807,0.0002643217,0.00009597019,0.0007390666,0.00346508,0.0009489512,0.002492363],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02012691,"threshold_uncertainty_score":0.04001951,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00905241537296972,"score_gpt":0.2540778192856136,"score_spread":0.2450254039126439,"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."}}