{"id":"W6901775536","doi":"10.6068/dp14ba8ee524f56","title":"Trend 2007 - 2011. Statistics Canada. CANSIM: Business, Consumer and Property Services - Arts, Entertainment and Recreation | Country: Canada | Table: Spectator sports, event promoters, artists and related industries, operating expenses, by North American Industry Classification System (NAICS) | Variable: Utilities and telecommunications expenses, Agents and managers for artists, athletes, entertainers and other public figures | Units: %, 2007-2011. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-010.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Recreation; Census; Amusement; Entertainment; Event (particle physics); Official statistics; Descriptive statistics; Tourism","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001538559,0.002557836,0.002722954,0.008708165,0.00261378,0.0047607,0.005226097,0.001534852,0.07755326],"category_scores_gemma":[0.01508963,0.001563212,0.002024946,0.0426526,0.000646038,0.002514846,0.002000242,0.003325379,0.06075843],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03541544,"about_ca_system_score_gemma":0.09822406,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9880869,"about_ca_topic_score_gemma":0.9871297,"domain_scores_codex":[0.9962109,0.0002083279,0.0004163632,0.0005758677,0.001731898,0.0008566529],"domain_scores_gemma":[0.9716574,0.001234918,0.001104766,0.0008025612,0.02388745,0.001312758],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001691797,0.00000602348,0.000998784,0.000162777,0.00001624917,0.000005201898,0.00001212577,0.00009737188,0.000006792297,0.000209707,0.9975956,0.0008723969],"study_design_scores_gemma":[0.0001811893,0.00001260085,0.02613243,0.0007898138,0.00006917493,0.00002780266,0.0005131992,0.0006251154,0.0001902431,0.0005713407,0.9708084,0.00007876156],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005820893,0.00004029801,0.0000150378,0.00009267211,0.00002339275,0.000007846733,0.9991486,0.00004102914,0.0005728532],"genre_scores_gemma":[0.0004912414,0.0001490944,0.000137335,0.00008919565,0.00001540903,0.00005530077,0.9968334,0.00005230437,0.002176667],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07755326,"threshold_uncertainty_score":0.2594415,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0331469804210678,"score_gpt":0.2492414563679381,"score_spread":0.2160944759468703,"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."}}