{"id":"W6920262229","doi":"10.6068/dp14baa29130f2","title":"Trend 2005 - 2010. Statistics Canada. CANSIM: Culture and Leisure - Film and Video | 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: 075-001-046.","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":[0.001966774,0.002625972,0.00270632,0.009008327,0.003084913,0.004774354,0.005290899,0.001458204,0.07152473],"category_scores_gemma":[0.01677692,0.00158253,0.002179614,0.04063598,0.0006447149,0.002318336,0.002138921,0.002966928,0.04653319],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04623622,"about_ca_system_score_gemma":0.1246411,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9945763,"about_ca_topic_score_gemma":0.9933305,"domain_scores_codex":[0.9960763,0.0002349964,0.0004057999,0.0005389167,0.001902017,0.0008418845],"domain_scores_gemma":[0.9692959,0.001096447,0.0009417674,0.0008670455,0.02640901,0.001389918],"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":[0.00002196477,0.000005746647,0.001113868,0.0002363066,0.00002227902,0.000006877974,0.00001900479,0.0001142179,0.000009277497,0.0003192101,0.9965552,0.001575955],"study_design_scores_gemma":[0.0001468699,0.00001285371,0.02670057,0.0008964111,0.00008643841,0.00003007676,0.0005196871,0.0005722989,0.0001844443,0.0006491339,0.9701126,0.00008870631],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005963343,0.00006590081,0.0000254992,0.0001256179,0.00003338911,0.00001098219,0.9988266,0.00005567715,0.0007968375],"genre_scores_gemma":[0.0008269011,0.0003022984,0.0003469311,0.0001371267,0.00002164875,0.00008156723,0.9944637,0.00008348411,0.003736337],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07152473,"threshold_uncertainty_score":0.3354688,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01968871893401275,"score_gpt":0.2332693365958121,"score_spread":0.2135806176617993,"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."}}