{"id":"W6957743162","doi":"10.6068/dp14ba8dd5a5e59","title":"Trend 1976 - 2011. Statistics Canada. CANSIM: Income, Pensions, Spending and Wealth - Household, Family and Personal Income | Country: Canada | Table: Government transfers, by economic family type and after-tax income quintiles, 2011 constant dollars | Variable: Lowest quintile, Average implicit rates of government transfers, Economic families, two persons or more | 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":"Healthcare Systems and Public Health","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Economic statistics; Personal income; Socioeconomic status; Government (linguistics); Total personal income; Census; Population; Family income; Transfer payment; Official 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":[0.002113726,0.002470784,0.002772181,0.008123963,0.003301952,0.004886122,0.005490798,0.001514481,0.08230648],"category_scores_gemma":[0.01842393,0.001850109,0.00214385,0.03852786,0.0006454462,0.002511115,0.002429927,0.003469634,0.05321724],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0516636,"about_ca_system_score_gemma":0.1152043,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9938881,"about_ca_topic_score_gemma":0.9924604,"domain_scores_codex":[0.9964227,0.0002430191,0.0003894791,0.0004849241,0.001602783,0.0008570801],"domain_scores_gemma":[0.9697223,0.001188686,0.0009477234,0.001024253,0.02573089,0.001386142],"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":[0.00002198359,0.000005777377,0.0009938712,0.0002191192,0.00001990747,0.000005773561,0.00002122774,0.0001025493,0.0000076523,0.0003634007,0.996776,0.001462632],"study_design_scores_gemma":[0.0001638502,0.00001137838,0.02540199,0.0008948631,0.0000705993,0.00002813283,0.0004331559,0.0004725057,0.0001871324,0.0007176517,0.9715261,0.00009262394],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004651001,0.00004079445,0.00001858273,0.00008411144,0.00001814493,0.00001087325,0.9991407,0.00004934218,0.0005909358],"genre_scores_gemma":[0.0006177875,0.0002053563,0.0002803597,0.00009121342,0.00001262193,0.00009553663,0.9958323,0.00007957718,0.002785252],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08230648,"threshold_uncertainty_score":0.3748475,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02555141237588902,"score_gpt":0.2639047195570916,"score_spread":0.2383533071812026,"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."}}