{"id":"W6976683093","doi":"10.6068/dp14ba84a74ed12","title":"Trend 1997 - 2005. Statistics Canada. CANSIM: Business, Consumer and Property Services - Information and Culture | Country: Canada | Table: Summary statistics for arts, entertainment and recreation | Variable: Other amusement and recreation industries, Operating revenue | Units: $CAD x 1,000,000, 1997-2005. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-011.","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; Amusement; Economic statistics; Census; Revenue; Entertainment; Publishing; Official statistics","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":[0.002302978,0.002452658,0.002696671,0.009405145,0.003513795,0.005231303,0.005203826,0.001522228,0.0995165],"category_scores_gemma":[0.01953507,0.001802762,0.001978768,0.04554924,0.0007144469,0.002860987,0.002427203,0.00334244,0.06319209],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05591099,"about_ca_system_score_gemma":0.1657992,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9946301,"about_ca_topic_score_gemma":0.9920716,"domain_scores_codex":[0.994835,0.000309147,0.0005389067,0.000615435,0.00256198,0.001139506],"domain_scores_gemma":[0.9584179,0.001365082,0.001068431,0.001089652,0.03624808,0.00181093],"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":[0.0000176503,0.000005697932,0.0007686446,0.0001900082,0.00001551599,0.000005959935,0.00001776331,0.00009203569,0.000007655044,0.000347703,0.9970251,0.001506416],"study_design_scores_gemma":[0.0001057071,0.00001056002,0.01768605,0.0008149017,0.00005825195,0.00002305053,0.0004767166,0.0004423845,0.0001573635,0.0006380911,0.9795119,0.00007501157],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005208023,0.00005212361,0.00002963615,0.000156028,0.00003564549,0.00001577736,0.9984831,0.00006889423,0.001106748],"genre_scores_gemma":[0.0008580829,0.000326934,0.0004423003,0.0001963568,0.00002143195,0.000130989,0.9922642,0.0001407223,0.005619007],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0995165,"threshold_uncertainty_score":0.4056645,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02341850957752496,"score_gpt":0.2394491763203454,"score_spread":0.2160306667428204,"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."}}