{"id":"W6958065725","doi":"10.6068/dp14ba8e4478681","title":"Trend 1994 - 2007. Statistics Canada. CANSIM: Income, Pensions, Spending and Wealth - Low Income and Inequality | Country: Canada | Table: Transitions of persons into and out of low income, by selected characteristics | Variable: Females, Low income cut-offs before tax, 1992 base, Exit (below low income lines to above low income lines)age of persons | Units: , 1994-2007. 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; Socioeconomic status; Population; Census; Economic inequality; Economic statistics; Demographic statistics; Personal income; Inequality; Poverty","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":[0.002094419,0.002393594,0.002921516,0.008176154,0.003087407,0.004518499,0.005428794,0.001342586,0.08515624],"category_scores_gemma":[0.01658711,0.001876378,0.002310675,0.03934137,0.0006032719,0.00217056,0.002405681,0.003233632,0.04224608],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05528703,"about_ca_system_score_gemma":0.1371512,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9949713,"about_ca_topic_score_gemma":0.9935994,"domain_scores_codex":[0.9960414,0.0002507249,0.0004897214,0.0004592293,0.001811259,0.0009477663],"domain_scores_gemma":[0.9670738,0.001071556,0.001062912,0.0008676463,0.0282852,0.001638844],"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":[0.000030409,0.000008290231,0.00137815,0.0002918588,0.00002594345,0.000006334899,0.00002413095,0.0001038921,0.000008579885,0.0003621528,0.9959856,0.001774766],"study_design_scores_gemma":[0.0002495063,0.00002074987,0.04694482,0.001316037,0.0001115305,0.00003707693,0.0006640951,0.0006061611,0.0002444013,0.0007710105,0.9489162,0.000118346],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00007114752,0.00005003939,0.0000209988,0.0001124821,0.00002482363,0.00001979629,0.9987948,0.00005016883,0.0008556772],"genre_scores_gemma":[0.001051644,0.0003153888,0.0003833365,0.000149799,0.00001853388,0.0001677837,0.9935307,0.0001008886,0.004281882],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9148437,"threshold_uncertainty_score":0.4011374,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0244099694065746,"score_gpt":0.2674004112790323,"score_spread":0.2429904418724577,"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."}}