{"id":"W6920203149","doi":"10.6068/dp14ba8e2126554","title":"Trend 1976 - 2011. Statistics Canada. CANSIM: Income, Pensions, Spending and Wealth - Household, Family and Personal Income | Country: Canada | Table: Persons in low income, by economic family type | Variable: Low income cut-offs after tax, 1992 base, Elderly males, Aggregate low income gap as a percentage of income | Units: , 1976-2011. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-119.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Personal income; Economic statistics; Socioeconomic status; Official statistics; Total personal income; Census; Population; Family income; Household income; Summary statistics","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":[0.00212323,0.002364127,0.002840442,0.008067527,0.003298505,0.004804465,0.005633628,0.001549276,0.08718642],"category_scores_gemma":[0.01912024,0.001801071,0.002236731,0.03812426,0.0006189077,0.002481543,0.002502254,0.003447142,0.05255217],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04832304,"about_ca_system_score_gemma":0.1139786,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9932594,"about_ca_topic_score_gemma":0.9918129,"domain_scores_codex":[0.9963663,0.0002471817,0.0004326222,0.0004813365,0.001611409,0.0008611928],"domain_scores_gemma":[0.968544,0.001270485,0.0009989272,0.001044762,0.0266814,0.001460394],"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":[0.00002278238,0.000006021,0.0009761494,0.0002509912,0.00002084348,0.000006185412,0.00002206948,0.00009955132,0.000007801582,0.0003473066,0.9968149,0.001425347],"study_design_scores_gemma":[0.0001844257,0.00001245111,0.02574797,0.001048625,0.00007701532,0.00003080552,0.0004645013,0.0004732927,0.0001816747,0.0007403289,0.9709411,0.00009778388],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004267776,0.00003816248,0.00001816183,0.00008457332,0.0000179367,0.00001255182,0.9991867,0.00004474408,0.0005545668],"genre_scores_gemma":[0.0005917829,0.0002001133,0.0002824007,0.00009761145,0.00001250453,0.0001099911,0.9960895,0.00007634034,0.002539898],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08718642,"threshold_uncertainty_score":0.3506098,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02367278050808301,"score_gpt":0.2426809424134184,"score_spread":0.2190081619053354,"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."}}