{"id":"W6939221715","doi":"10.6068/dp14baa2cb89062","title":"Trend 2005 - 2009. Statistics Canada. CANSIM: Culture and Leisure - Film and Video | Country: Canada | Province: Saskatchewan | 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: , 2005-2009. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-046.","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; Population; Economic statistics; Summary statistics; Descriptive statistics; Socioeconomic status; Distribution (mathematics)","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":[0.001853625,0.002438103,0.002637922,0.008352073,0.003110988,0.004677676,0.005061189,0.001403031,0.08402634],"category_scores_gemma":[0.01742611,0.00155474,0.00212633,0.03980527,0.000606738,0.002322965,0.002106928,0.002847218,0.05045228],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04809206,"about_ca_system_score_gemma":0.1291383,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9946966,"about_ca_topic_score_gemma":0.9930834,"domain_scores_codex":[0.9961962,0.0002419172,0.0004463985,0.0005256264,0.00173569,0.000854152],"domain_scores_gemma":[0.9668522,0.001187083,0.0009595516,0.0009092861,0.02866241,0.00142948],"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":[0.00002295201,0.000005859277,0.001062472,0.0002341791,0.00002142599,0.000006644726,0.00001933262,0.0001010454,0.000009256866,0.0003272885,0.9966365,0.001552952],"study_design_scores_gemma":[0.0001597699,0.0000134533,0.02852452,0.0009336949,0.00008559365,0.00002946187,0.0005698064,0.0005341204,0.0001981253,0.0006481143,0.9682111,0.00009227639],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005540158,0.00004996636,0.00002249979,0.0001149824,0.00002872946,0.00001177339,0.998921,0.00004759887,0.0007480095],"genre_scores_gemma":[0.0008514755,0.0002807034,0.0003514147,0.000148693,0.00001938696,0.0001032363,0.9940216,0.00008622834,0.004137214],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08402634,"threshold_uncertainty_score":0.348934,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01781188338949253,"score_gpt":0.2331548667546672,"score_spread":0.2153429833651747,"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."}}