{"id":"W6920056329","doi":"10.6068/dp14ba89616da19","title":"Trend 1976 - 2011. Statistics Canada. CANSIM: Income, Pensions, Spending and Wealth - Low Income and Inequality | Country: Canada | Table: Market, total and after-tax income of individuals, where each individual is represented by their adjusted household income, by economic family type and adjusted after-tax income quintiles, 2011 constant dollars | Variable: Adjusted market income, Second quintile, Share of income, Unattached individuals | Units: , 1976-2011. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-121.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Official statistics; Economic statistics; Census; Population; Economic inequality; Inequality; Socioeconomic status; Household income; Demographic statistics; Personal income","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002010283,0.002490522,0.00313292,0.008064646,0.003072636,0.004775793,0.005570635,0.001492356,0.08768068],"category_scores_gemma":[0.01792536,0.001955203,0.00240843,0.03949762,0.0006413513,0.002453091,0.002444034,0.003717803,0.04726686],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05003591,"about_ca_system_score_gemma":0.1198989,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9937041,"about_ca_topic_score_gemma":0.9919661,"domain_scores_codex":[0.9963421,0.0002323131,0.0004411745,0.0004492255,0.001672632,0.0008625437],"domain_scores_gemma":[0.9662171,0.001133798,0.001103096,0.0009326522,0.02910491,0.001508401],"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":[0.00002805172,0.000007568285,0.001297759,0.0002951944,0.00002512263,0.000006038027,0.00002144741,0.0001089733,0.000007951503,0.0003415638,0.9962211,0.001639148],"study_design_scores_gemma":[0.000274281,0.00001899484,0.04488045,0.001382057,0.0001078624,0.00003829283,0.0005982522,0.0005859597,0.0002257693,0.0008277474,0.9509384,0.0001220359],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006005546,0.00004875305,0.00001954138,0.0000969818,0.00002623206,0.00001690787,0.9990188,0.00004677914,0.0006659673],"genre_scores_gemma":[0.0008972034,0.0002896297,0.0003174866,0.0001390322,0.00002122827,0.0001588821,0.9942884,0.00009407126,0.003793909],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9123193,"threshold_uncertainty_score":0.3630376,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02694527654258957,"score_gpt":0.2464457578610028,"score_spread":0.2195004813184132,"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."}}