{"id":"W6976665898","doi":"10.6068/dp14ba8e9187a97","title":"Trend 1999 - 2011. Statistics Canada. CANSIM: Government - Government Business Enterprises | Country: Canada | Table: Balance sheet and income statement of federal government business enterprises, by North American Industry Classification System (NAICS), end of fiscal year closest to December 31 | Variable: Other operating revenue, Finance, insurance, real estate, rental and leasing and management of companies and enterprises | Units: $CAD x 1,000, 1999-2011. 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); Economic statistics; Balance sheet; Income statement; Official statistics; Census; Descriptive statistics; Payroll; Goods and services; Politics","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":[0.001478127,0.002324888,0.002529719,0.009377814,0.003352382,0.005077987,0.004818475,0.001454418,0.07215283],"category_scores_gemma":[0.01480006,0.001528969,0.001717829,0.04375288,0.000650152,0.002515981,0.00218547,0.002916309,0.05560562],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04503658,"about_ca_system_score_gemma":0.1138636,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9931496,"about_ca_topic_score_gemma":0.992264,"domain_scores_codex":[0.9963792,0.0001900786,0.0003716777,0.0004987126,0.001673809,0.0008865406],"domain_scores_gemma":[0.968924,0.0009694688,0.001066518,0.0009427461,0.02672571,0.001371425],"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":[0.0000190557,0.000006089934,0.0008862999,0.0001647983,0.00001518096,0.000006521836,0.000015929,0.00009137119,0.000007676982,0.0003304658,0.9972893,0.001167279],"study_design_scores_gemma":[0.000105335,0.000009501773,0.02145472,0.0006386235,0.00005028845,0.00002345379,0.0004483798,0.0004733572,0.0001733135,0.0005662306,0.9759863,0.00007059943],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005390063,0.00004006881,0.00001477038,0.00009216489,0.00002095825,0.000008511896,0.9990371,0.00004250148,0.0006899465],"genre_scores_gemma":[0.0005591823,0.0001731187,0.0001720439,0.00007964768,0.0000118598,0.00005200306,0.9961243,0.00005300718,0.002774818],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07215283,"threshold_uncertainty_score":0.3267648,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01345847220663805,"score_gpt":0.2296086266622894,"score_spread":0.2161501544556514,"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."}}