{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001191497,0.00006879875,0.0001683316,0.0001394608,0.0001646296,0.0002727973,0.0001413503,0.00002085107,0.00004069695],"category_scores_gemma":[0.0003072373,0.00005257714,0.00006658273,0.0002999328,0.00005202358,0.0002547647,0.0001705293,0.000106251,0.000004653987],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006041451,"about_ca_system_score_gemma":0.00001005938,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001414663,"about_ca_topic_score_gemma":0.000008173048,"domain_scores_codex":[0.9990011,0.00005209019,0.0003938132,0.0001486122,0.0002997909,0.0001045661],"domain_scores_gemma":[0.9992172,0.00021359,0.0002403379,0.0001357729,0.0001158015,0.00007730455],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000063509,0.00003712438,0.00804558,0.000009006152,0.00001182899,0.0001093318,0.0003303004,0.00009620825,0.00002110069,0.004467049,0.00145572,0.9853532],"study_design_scores_gemma":[0.000882278,0.0000990559,0.2715015,0.00007452881,0.00007907169,0.0002679652,0.002381902,0.0007309722,0.00006257861,0.4802121,0.2435112,0.0001968939],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6294118,0.0009199316,0.3642611,0.0002789418,0.0002195129,0.00005907161,0.00001270849,0.000004802218,0.004832176],"genre_scores_gemma":[0.9960905,0.001028484,0.00213563,0.00008071087,0.0001049652,0.000001097844,3.053151e-7,0.000003376812,0.0005549757],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9851564,"threshold_uncertainty_score":0.2630591,"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."}}