{"id":"W1794961931","doi":"10.48550/arxiv.1202.3722","title":"Hierarchical Affinity Propagation","year":2012,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Hierarchy; Affinity propagation; Cluster analysis; Computer science; Set (abstract data type); Variety (cybernetics); Inference; Hierarchical clustering; Data mining; Function (biology); Algorithm; Artificial intelligence; Correlation clustering; Canopy clustering algorithm; Biology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.002492299,0.001899121,0.001951935,0.002982469,0.001688029,0.002481565,0.005254802,0.003498142,0.01028573],"category_scores_gemma":[0.01205476,0.001148474,0.002393263,0.003657105,0.001354338,0.002701378,0.00320325,0.003007081,0.004591634],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001937275,"about_ca_system_score_gemma":0.002433215,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01510977,"about_ca_topic_score_gemma":0.01759857,"domain_scores_codex":[0.9966514,0.0007248341,0.0001679846,0.0007386714,0.001377687,0.0003394439],"domain_scores_gemma":[0.9944746,0.002141754,0.0003394067,0.001003555,0.00185959,0.0001811225],"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":[0.000267314,0.0002574301,0.002554807,0.0004572691,0.0003453158,0.0002356612,0.0003684923,0.4556861,0.009169894,0.05828822,0.0357797,0.4365897],"study_design_scores_gemma":[0.0000316155,0.00002929482,0.0002259748,0.00001901602,0.00003156161,0.00007262974,0.00002496536,0.967404,0.002643318,0.02438028,0.005116134,0.00002115592],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003345907,0.0001935056,0.9920771,0.0002015899,0.00008659009,0.0001125046,0.0002294975,0.001585533,0.00216773],"genre_scores_gemma":[0.1911172,0.0004519069,0.7907748,0.0006471906,0.0002405746,0.0005122211,0.001449828,0.0007153793,0.01409091],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01510977,"threshold_uncertainty_score":0.03440922,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05403532215696161,"score_gpt":0.1880879063902214,"score_spread":0.1340525842332598,"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."}}