{"id":"W1766971555","doi":"10.1023/a:1016013912188","title":"Informetric analysis of a music database","year":2002,"lang":"en","type":"article","venue":"Scientometrics","topic":"Scientific Research and Discoveries","field":"Physics and Astronomy","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"Zipf's law; Computer science; Poisson distribution; Information retrieval; Equivalence (formal languages); Informetrics; Database; Data mining; Statistics; Mathematics; Bibliometrics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":["bibliometrics"],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":[],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"}],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.002083163,0.0004502948,0.00073009,0.02048284,0.001257434,0.003204767,0.0009715118,0.0007794088,0.004026224],"category_scores_gemma":[0.01687242,0.0001708824,0.0007040009,0.02148216,0.000458103,0.002284817,0.001080337,0.0004032114,0.001464325],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001288411,"about_ca_system_score_gemma":0.001711179,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004566484,"about_ca_topic_score_gemma":0.005753262,"domain_scores_codex":[0.9968786,0.0005503981,0.000485973,0.000394437,0.001438024,0.0002525955],"domain_scores_gemma":[0.9909532,0.004435603,0.0008362,0.001209269,0.002187707,0.0003780828],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.003780091,0.001042739,0.08057706,0.00107073,0.0004301,0.002040722,0.001227692,0.02572299,0.03916785,0.05105567,0.02665406,0.7672303],"study_design_scores_gemma":[0.000381947,0.001073288,0.1420041,0.0001997753,0.000863714,0.005692061,0.004372207,0.6011562,0.08029961,0.06982318,0.09393849,0.0001954407],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8086076,0.00332346,0.1210166,0.002365612,0.0002571335,0.000302544,0.03961051,0.002923881,0.02159259],"genre_scores_gemma":[0.8483283,0.001249223,0.1074435,0.0001401938,0.0002734475,0.0001220566,0.03796878,0.0001043525,0.004370123],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9795172,"threshold_uncertainty_score":0.0134691,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08002575222830237,"score_gpt":0.3216646337892866,"score_spread":0.2416388815609842,"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."}}