{"id":"W6938946999","doi":"10.6068/dp14ba8bfdf6d44","title":"Trend 1993 - 2012. Statistics Canada. CANSIM: Government - Government Business Enterprises | Country: Canada | Table: Net income of provincial and territorial liquor authorities and government revenue from the control and sale of alcoholic beverages, fiscal years ended March 31 | Variable: Total of net income of liquor authorities and provincial and territorial government revenue | Units: $CAD x 1,000, 1993-2012. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-105.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Government (linguistics); Revenue; Economic statistics; Official statistics; Government revenue; Goods and services; Net income; Census; Descriptive statistics; Control (management)","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":[0.001827922,0.002360955,0.002633478,0.009015498,0.0032905,0.005081572,0.004922894,0.001398841,0.07069014],"category_scores_gemma":[0.01623889,0.001757115,0.001895744,0.04441472,0.0006302134,0.002426681,0.002138328,0.003076009,0.04824625],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05447673,"about_ca_system_score_gemma":0.1342343,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9953505,"about_ca_topic_score_gemma":0.9941038,"domain_scores_codex":[0.9959558,0.00021792,0.0004345727,0.0005231582,0.001888276,0.0009803937],"domain_scores_gemma":[0.9681731,0.001025,0.00112422,0.0008874178,0.02735669,0.001433638],"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":[0.00002451528,0.000006697754,0.001195071,0.0002383085,0.00002026918,0.000007828287,0.00002242368,0.000104032,0.000008443048,0.0003625336,0.9964663,0.001543525],"study_design_scores_gemma":[0.0001378699,0.00001179967,0.02956467,0.0008531003,0.00007002422,0.00002687663,0.0005089836,0.0004715297,0.000187058,0.0005942584,0.9674964,0.00007756485],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006031339,0.00005280884,0.00001763646,0.00010974,0.00002106975,0.0000107153,0.9989471,0.00004446726,0.0007363033],"genre_scores_gemma":[0.0008820887,0.0002891591,0.0002750697,0.0001176846,0.00001602758,0.00008734353,0.9943574,0.00007750867,0.003897612],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07069014,"threshold_uncertainty_score":0.3952582,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01065463505445412,"score_gpt":0.2199776769855001,"score_spread":0.209323041931046,"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."}}