{"id":"W6920298783","doi":"10.6068/dp14baa28f81191","title":"Trend 2005 - 2010. Statistics Canada. CANSIM: Culture and Leisure - Film and Video | Country: Canada | Province: Ontario | 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: 075-001-046.","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; Descriptive statistics; Economic statistics; Summary statistics; Socioeconomic status; Entertainment","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":[0.001934301,0.002504193,0.002720166,0.008744301,0.003224246,0.004630208,0.005037249,0.001428669,0.07304937],"category_scores_gemma":[0.01771665,0.00159358,0.002245153,0.03974121,0.0006665355,0.002463961,0.002208536,0.002815905,0.04584005],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04895941,"about_ca_system_score_gemma":0.127425,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9947872,"about_ca_topic_score_gemma":0.9936693,"domain_scores_codex":[0.9959397,0.000245227,0.0004528811,0.0005577888,0.001929664,0.0008746637],"domain_scores_gemma":[0.9671665,0.001135147,0.001069636,0.0009131565,0.02825562,0.001459929],"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":[0.00002215377,0.000005513405,0.001133641,0.0002507208,0.00002250564,0.000006589311,0.00002075675,0.00009988148,0.000008980439,0.0003104389,0.9966238,0.00149511],"study_design_scores_gemma":[0.0001445887,0.00001273311,0.02735012,0.0008752679,0.00008585867,0.00002868986,0.0005026981,0.000520085,0.000175618,0.000582091,0.9696367,0.00008554279],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005839649,0.00006296534,0.00002391872,0.000128671,0.00003017526,0.00001143252,0.9988706,0.00005067463,0.0007632156],"genre_scores_gemma":[0.0008803809,0.0003141287,0.0003350679,0.0001417305,0.00002239627,0.00009572704,0.9942939,0.00008403863,0.003832565],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07304937,"threshold_uncertainty_score":0.355227,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02170253763936928,"score_gpt":0.2310169866022387,"score_spread":0.2093144489628694,"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."}}