{"id":"W6920514008","doi":"10.6068/dp14ba7ce5c4d14","title":"Trend 2005 - 2010. Statistics Canada. CANSIM: Culture and Leisure - Trade in Culture Goods and Services | Country: Canada | Table: Film, television and video post-production, summary statistics, by North American Industry Classification System (NAICS) | Variable: Post-production and other motion picture and video industries, Operating expenses (x 1,000,000) | Units: , 2005-2010. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-055.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Census; Official statistics; Economic statistics; Portrait; Goods and services; Summary statistics; Statistician; Index (typography)","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":[0.0016739,0.002626338,0.002772395,0.008792713,0.002792957,0.004606652,0.004989537,0.001425897,0.0741254],"category_scores_gemma":[0.01498524,0.001581752,0.002124669,0.04163866,0.0006767803,0.002498156,0.002128626,0.003271609,0.04819643],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03905433,"about_ca_system_score_gemma":0.1116302,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9915586,"about_ca_topic_score_gemma":0.9894227,"domain_scores_codex":[0.9961052,0.0002184575,0.0004306562,0.0005215003,0.001842116,0.0008820815],"domain_scores_gemma":[0.9705723,0.001015432,0.001060883,0.0006960231,0.02536978,0.00128561],"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":[0.00002212405,0.000006078192,0.001197923,0.0002404947,0.00002142243,0.000006260104,0.00001531737,0.0000982639,0.000007948668,0.000245752,0.9969243,0.001214052],"study_design_scores_gemma":[0.0001966069,0.00001488098,0.03221365,0.00102328,0.00009374946,0.00003038498,0.0005596435,0.000567981,0.0002035061,0.00066661,0.9643417,0.00008798912],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0000580427,0.00005170979,0.00001688915,0.0001096798,0.0000274528,0.00000947465,0.999106,0.00003980635,0.0005808584],"genre_scores_gemma":[0.0007193827,0.0002552653,0.0002142781,0.0001207207,0.00002014592,0.00008145701,0.9956254,0.00006144015,0.002901931],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0741254,"threshold_uncertainty_score":0.2833604,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02102994079264046,"score_gpt":0.2390409719032151,"score_spread":0.2180110311105747,"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."}}