{"id":"W6939159118","doi":"10.6068/dp14baa379f9136","title":"Trend 2005 - 2010. Statistics Canada. CANSIM: Culture and Leisure - Trade in Culture Goods and Services | Country: Canada | Province: Alberta | 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; Goods and services; Summary statistics; Economic statistics; Portrait; Ornaments","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":[0.001736613,0.002666369,0.002863125,0.008933352,0.002823452,0.004638333,0.00511709,0.001450096,0.07257342],"category_scores_gemma":[0.01445414,0.001625139,0.002156226,0.04286487,0.0006713607,0.002328611,0.00206258,0.003173571,0.04583304],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04135296,"about_ca_system_score_gemma":0.1139609,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9930325,"about_ca_topic_score_gemma":0.9916825,"domain_scores_codex":[0.9963372,0.0001897189,0.0003837236,0.0004991067,0.001763685,0.00082662],"domain_scores_gemma":[0.9715441,0.001017561,0.001006759,0.000696535,0.02442194,0.00131302],"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":[0.00002355747,0.000006241788,0.001328663,0.0002549981,0.00002338817,0.000006695963,0.00001700272,0.0001102729,0.000009095625,0.000246056,0.9967158,0.001258303],"study_design_scores_gemma":[0.0002202602,0.00001593291,0.03781246,0.001043295,0.0001055458,0.00003153907,0.0006046664,0.0006027581,0.0002096977,0.0006643843,0.958595,0.00009450084],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006422996,0.00005403627,0.00001756945,0.0001002794,0.00002941913,0.000009390536,0.9990861,0.0000415242,0.0005973552],"genre_scores_gemma":[0.0007399268,0.0002384982,0.000219356,0.0001081237,0.00001936234,0.00007278637,0.9955504,0.00005887727,0.002992721],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07257342,"threshold_uncertainty_score":0.3000381,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01791951892841077,"score_gpt":0.2341780611190155,"score_spread":0.2162585421906048,"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."}}