{"id":"W3035874454","doi":"10.48550/arxiv.2006.10716","title":"Market Graph Clustering Via QUBO and Digital Annealing","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Game Theory and Applications","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Medoid; Cluster analysis; Computer science; Clustering coefficient; Quadratic equation; Graph; Index (typography); Replicate; Data mining; Theoretical computer science; Mathematics; Artificial intelligence; Statistics","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":[0.001577124,0.00106712,0.001694688,0.001083688,0.001081163,0.0015227,0.002413029,0.002085725,0.006316923],"category_scores_gemma":[0.005688118,0.0008245371,0.001234048,0.001105646,0.001584503,0.001866901,0.001579394,0.001596102,0.0009974355],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002699955,"about_ca_system_score_gemma":0.001960957,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01332611,"about_ca_topic_score_gemma":0.01672874,"domain_scores_codex":[0.9993099,0.0002313687,0.00002523708,0.000220634,0.0001296867,0.00008311585],"domain_scores_gemma":[0.9980966,0.00120616,0.0001525314,0.0001976059,0.0002455854,0.0001014333],"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":[0.0000595614,0.00003722632,0.0003018573,0.00006021157,0.00002649931,0.00003059932,0.00006840983,0.954889,0.0007538705,0.01769878,0.002403385,0.02367058],"study_design_scores_gemma":[0.000007821448,0.00000784962,0.00003301651,0.000003557617,0.000002346933,0.000004510289,0.000008653179,0.9910675,0.0001274811,0.008277917,0.0004561712,0.000003134365],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0167065,0.00033552,0.9749786,0.000661645,0.00009249081,0.0001151585,0.0001287352,0.0006904135,0.006290847],"genre_scores_gemma":[0.4735332,0.000225274,0.5151842,0.0006829613,0.00006067655,0.0004445154,0.0004290486,0.0004855015,0.00895454],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01332611,"threshold_uncertainty_score":0.02649707,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1794341394685739,"score_gpt":0.2508226628580395,"score_spread":0.07138852338946564,"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."}}