{"id":"W6939160708","doi":"10.6068/dp14ba8ebb1e075","title":"Trend 2007 - 2011. Statistics Canada. CANSIM: Business, Consumer and Property Services - Arts, Entertainment and Recreation | Country: Canada | Table: Spectator sports, event promoters, artists and related industries, operating expenses, by North American Industry Classification System (NAICS) | Variable: Advertising, marketing and promotions, Agents and managers for artists, athletes, entertainers and other public figures | Units: %, 2007-2011. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-010.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Recreation; Census; Amusement; Entertainment; Event (particle physics); Official statistics; Tourism; Descriptive statistics; Socioeconomic status","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001250678,0.001056972,0.001133772,0.0002051791,0.0004682433,0.0008185851,0.0004857795,0.0005299264,0.0004514622],"category_scores_gemma":[0.0001877226,0.0009371875,2.989323e-7,0.0002910543,0.0006456313,0.0006874716,0.0005108038,0.0007897288,0.000003012604],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004905739,"about_ca_system_score_gemma":0.003236048,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9900274,"about_ca_topic_score_gemma":0.9658278,"domain_scores_codex":[0.9941661,0.0006806069,0.001303443,0.001979247,0.000950882,0.0009197289],"domain_scores_gemma":[0.9956018,0.0003736199,0.001647386,0.001248732,0.0002039994,0.0009244842],"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":[0.0002024482,0.0000840914,0.01028178,0.002832939,0.0005294879,0.0001626335,0.00001855381,0.000001527152,0.000007807885,0.00003730086,0.9845344,0.001307024],"study_design_scores_gemma":[0.001553178,0.00009057572,0.001628744,0.0005713421,0.0006937407,0.0004594715,0.001810373,0.006434215,4.796278e-8,5.612763e-8,0.9857245,0.001033743],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0003578235,0.008738758,0.000007250375,0.0000208894,0.0002253432,0.002797996,0.9875529,0.00009945923,0.0001996164],"genre_scores_gemma":[0.0007328533,0.002698198,0.0003410466,0.0001485393,0.00008494014,0.000122155,0.9939191,0.0003561271,0.00159707],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02419961,"threshold_uncertainty_score":0.9993079,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02255072867659748,"score_gpt":0.2381845834047481,"score_spread":0.2156338547281506,"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."}}