{"id":"W6901742194","doi":"10.6068/dp14ba83fe5eb21","title":"Trend 1997 - 2005. Statistics Canada. CANSIM: Business, Consumer and Property Services - Arts, Entertainment and Recreation | Country: Canada | Table: Summary statistics for arts, entertainment and recreation | Variable: Spectator sports, Salaries, wages and benefits | Units: $CAD x 1,000,000, 1997-2005. 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; Entertainment; Amusement; Census; Descriptive statistics; Socioeconomic status; Tourism","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":[0.001917984,0.002446371,0.002525689,0.008926422,0.003258217,0.00500463,0.005174717,0.001471383,0.08934459],"category_scores_gemma":[0.01669166,0.001682878,0.001895843,0.04405193,0.0006725367,0.002582979,0.002175487,0.00324197,0.05570738],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0470505,"about_ca_system_score_gemma":0.1389473,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9944817,"about_ca_topic_score_gemma":0.9924424,"domain_scores_codex":[0.995717,0.0002377786,0.0004452911,0.0005751503,0.001997141,0.001027737],"domain_scores_gemma":[0.9702454,0.001097851,0.0008975296,0.0008420762,0.02550098,0.001416114],"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":[0.00001739567,0.000005505323,0.000845583,0.0001810708,0.00001582694,0.000006073369,0.00001758719,0.00009132762,0.000007872539,0.0003606592,0.9971418,0.001309191],"study_design_scores_gemma":[0.0001137695,0.000009743584,0.01864329,0.0007367424,0.00006040074,0.00002374684,0.0004584856,0.0004260535,0.0001671185,0.0006048297,0.9786859,0.00007000825],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005021987,0.00004692955,0.00002387102,0.0001209542,0.00002622626,0.00001154162,0.9987426,0.00005612758,0.0009215868],"genre_scores_gemma":[0.0007445017,0.0002754257,0.0003250372,0.0001454985,0.00001701179,0.00009213253,0.9942219,0.0001070039,0.004071474],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08934459,"threshold_uncertainty_score":0.3413768,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02096314825101064,"score_gpt":0.2319367583196602,"score_spread":0.2109736100686495,"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."}}