{"id":"W2009818353","doi":"10.1068/a4253","title":"Music Scenes to Music Clusters: The Economic Geography of Music in the US, 1970–2000","year":2010,"lang":"en","type":"article","venue":"Environment and Planning A Economy and Space","topic":"Cultural Industries and Urban Development","field":"Social Sciences","cited_by":98,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Music industry; Scope (computer science); Music Geography; Scale (ratio); Economic geography; Creative industries; Popular music; Memphis; Test (biology); Population; Visual arts; Geography; Music education; Human geography; Sociology; Cultural geography; Art; Cartography; Demography; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005479485,0.0001057521,0.0001274044,0.00004614723,0.0003180797,0.00009939482,0.0001562469,0.00007202781,0.0003665117],"category_scores_gemma":[0.000008013563,0.00006851467,0.00003053262,0.00005174442,0.0002724374,0.0001097233,0.00005719162,0.0001742001,0.00001005731],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003490222,"about_ca_system_score_gemma":0.00004210226,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002352578,"about_ca_topic_score_gemma":0.003437058,"domain_scores_codex":[0.9993458,0.00005668932,0.0001463598,0.000187615,0.00005921818,0.0002043249],"domain_scores_gemma":[0.9996133,0.0001309529,0.00006704872,0.0001207513,0.000002228962,0.00006569278],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004524457,0.00004057892,0.7369304,0.00001418795,0.00005336219,0.000006592539,0.1877502,0.000861982,0.00004875038,0.001937215,0.05260807,0.01970344],"study_design_scores_gemma":[0.0001662735,0.00002893832,0.1465184,0.0000158568,0.000009977352,0.000001920542,0.01733446,0.00006063574,0.00001057127,0.0001662156,0.8355591,0.0001276027],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9847221,0.0003732838,0.000003218761,0.005851546,0.0001718266,0.0002639385,0.000004305404,0.000005307439,0.008604453],"genre_scores_gemma":[0.9970348,0.0001241475,0.0000721548,0.0007048713,0.0001913553,0.00002224557,0.000003322656,0.000003863395,0.001843225],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7829511,"threshold_uncertainty_score":0.4013046,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02396156128412047,"score_gpt":0.2219712851327963,"score_spread":0.1980097238486759,"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."}}