{"id":"W6939160708","doi":"10.6068/dp14ba8ebb1e075","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: Advertising, marketing and promotions, 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; Tourism; Descriptive statistics; Socioeconomic status","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":[0.001588135,0.002669887,0.002831168,0.008787137,0.002675295,0.004799985,0.005494949,0.00159939,0.07895149],"category_scores_gemma":[0.01565842,0.001588526,0.002113559,0.04213926,0.000680472,0.002580268,0.002054638,0.003299849,0.06788854],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03370087,"about_ca_system_score_gemma":0.09426188,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9869291,"about_ca_topic_score_gemma":0.9861481,"domain_scores_codex":[0.9962481,0.0002160691,0.0004156269,0.0006030756,0.001672892,0.0008441836],"domain_scores_gemma":[0.9712643,0.00131133,0.001123488,0.0008952864,0.02403215,0.001373445],"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":[0.00001659251,0.000005943635,0.0009474642,0.0001589473,0.00001573196,0.000004647503,0.00001112534,0.00009058272,0.000006568133,0.0001793776,0.9977253,0.0008376522],"study_design_scores_gemma":[0.0001941865,0.00001331097,0.02511489,0.0007684139,0.00007042165,0.00002692733,0.0004766147,0.0006497627,0.0001896153,0.0005796864,0.9718366,0.00007958084],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005266751,0.00003742329,0.00001403694,0.00008341984,0.00002243849,0.000007524616,0.9992404,0.00004394877,0.0004981328],"genre_scores_gemma":[0.0004197739,0.000124746,0.0001243412,0.00007883035,0.00001406057,0.00005213177,0.9973069,0.00005022033,0.001828999],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07895149,"threshold_uncertainty_score":0.264119,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02255072867659748,"score_gpt":0.2381845834047481,"score_spread":0.2156338547281506,"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."}}