{"id":"W4221058759","doi":"10.1007/s11192-022-04331-8","title":"Music information visualization and classical composers discovery: an application of network graphs, multidimensional scaling, and support vector machines","year":2022,"lang":"en","type":"article","venue":"Scientometrics","topic":"Music and Audio Processing","field":"Computer Science","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Multidimensional scaling; Computer science; Visualization; Pairwise comparison; Similarity (geometry); Music theory; Musical; Musicology; Realm; Musical composition; Classical music; Artificial intelligence; Art; Machine learning; Visual arts; History","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":[0.003033037,0.001019088,0.0007073851,0.009784624,0.000638808,0.002812946,0.0006329083,0.0006738,0.001675738],"category_scores_gemma":[0.01448851,0.0003252693,0.0007461623,0.007448383,0.0007802515,0.001766605,0.001166433,0.0007809265,0.0002575193],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009550105,"about_ca_system_score_gemma":0.0007227227,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00672006,"about_ca_topic_score_gemma":0.004594712,"domain_scores_codex":[0.9980933,0.001078973,0.0001270501,0.0002258659,0.0004200618,0.00005482222],"domain_scores_gemma":[0.9888366,0.008614822,0.0008047172,0.000668832,0.0008720781,0.0002029094],"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":[0.0004477534,0.0003415086,0.02763517,0.0005518294,0.0004210615,0.0004589295,0.002174879,0.2180717,0.006669032,0.05384782,0.01037917,0.6790012],"study_design_scores_gemma":[0.00002240118,0.00004744853,0.004846722,0.00003717737,0.00002620722,0.00008819734,0.0004672866,0.9610222,0.002325789,0.02770654,0.003373461,0.00003655213],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1463885,0.001170389,0.8377823,0.002396157,0.0001605661,0.0002200341,0.001599076,0.006111904,0.004171215],"genre_scores_gemma":[0.4629458,0.0004090602,0.5352931,0.00004929324,0.0000679908,0.0001344569,0.0005303951,0.0001270345,0.0004427218],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9902154,"threshold_uncertainty_score":0.01604044,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01655784240464562,"score_gpt":0.2749442264079385,"score_spread":0.2583863840032928,"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."}}