{"id":"W3119854094","doi":"10.3390/jrfm14010034","title":"Market Graph Clustering via QUBO and Digital Annealing","year":2021,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Game Theory and Applications","field":"Decision Sciences","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"University of Toronto","keywords":"Medoid; Cluster analysis; Computer science; Quadratic equation; Simulated annealing; Clustering coefficient; Graph; Binary number; Theoretical computer science; Data mining; Algorithm; Mathematics; Artificial intelligence; Arithmetic","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.0015161,0.0009399067,0.001581446,0.001031159,0.001026112,0.001515804,0.002394618,0.002004375,0.007648233],"category_scores_gemma":[0.005160468,0.0007888963,0.001191689,0.001174873,0.001379858,0.001899195,0.00180988,0.001796741,0.001200557],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002130551,"about_ca_system_score_gemma":0.00199331,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009376875,"about_ca_topic_score_gemma":0.01254289,"domain_scores_codex":[0.9993204,0.0002130241,0.00002749192,0.0001843621,0.0001799159,0.00007487539],"domain_scores_gemma":[0.9983053,0.0009842484,0.0001444801,0.0002337692,0.0002459202,0.00008625072],"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":[0.00006352556,0.00005277998,0.0002944468,0.00008208894,0.0000337401,0.0000367932,0.00007847419,0.915655,0.001264138,0.03254814,0.00332503,0.04656587],"study_design_scores_gemma":[0.000006666232,0.000007734491,0.00002868518,0.000003770105,0.000002338504,0.000006025115,0.000006343661,0.9903909,0.0001786357,0.008672812,0.0006926729,0.000003391381],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005588727,0.000185614,0.9890592,0.0003061279,0.00006908498,0.00006559551,0.00006559144,0.0004651837,0.004194913],"genre_scores_gemma":[0.2744657,0.0001846678,0.717093,0.0004625828,0.00006005854,0.000344994,0.0002780863,0.0004426866,0.006668311],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009376875,"threshold_uncertainty_score":0.02558589,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0209712461692916,"score_gpt":0.2847927307988203,"score_spread":0.2638214846295287,"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."}}