{"id":"W2962062541","doi":"10.1007/s11192-019-03166-0","title":"Visualizing music similarity: clustering and mapping 500 classical music composers","year":2019,"lang":"en","type":"article","venue":"Scientometrics","topic":"Music and Audio Processing","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Multidimensional scaling; Similarity (geometry); Cluster analysis; Dendrogram; Distance matrices in phylogeny; Canonical correlation; Computer science; Artificial intelligence; Mathematics; Pattern recognition (psychology); Combinatorics; Machine learning","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":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.001001693,0.0003930542,0.0004265649,0.009199968,0.0008259239,0.001340768,0.0003334965,0.0003930568,0.00583037],"category_scores_gemma":[0.004775417,0.00015793,0.0003401531,0.008086043,0.0004508854,0.0008719002,0.000884252,0.0002294953,0.0009783258],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005294762,"about_ca_system_score_gemma":0.0004241711,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007118722,"about_ca_topic_score_gemma":0.009792768,"domain_scores_codex":[0.9992926,0.000128828,0.00005622848,0.0001498577,0.0003146295,0.00005790133],"domain_scores_gemma":[0.9983141,0.000671433,0.0001742396,0.0001638324,0.0005605334,0.0001158219],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007097748,0.0002151849,0.0598628,0.000811294,0.0002906016,0.0005367293,0.01614777,0.01256592,0.02877102,0.01150173,0.01181404,0.8567732],"study_design_scores_gemma":[0.0001362795,0.000537683,0.6474554,0.0002770339,0.0003873785,0.001831789,0.03965439,0.1610057,0.03182912,0.02460166,0.09194544,0.0003382692],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9124632,0.0005889049,0.05989517,0.0002140713,0.00008341653,0.0002328567,0.002627908,0.001436414,0.02245801],"genre_scores_gemma":[0.9022976,0.0003198376,0.09058589,0.00001674593,0.00004098065,0.0001297175,0.002633261,0.0002161195,0.003759987],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9908,"threshold_uncertainty_score":0.01950455,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06368608730831339,"score_gpt":0.2883530903411748,"score_spread":0.2246670030328615,"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."}}