{"id":"W6977094154","doi":"10.6068/dp14ba7ca792135","title":"Trend 2000 - 2011. Statistics Canada. CANSIM: Labor - Employment Insurance, Social Assistance and Other Transfers | Country: Canada | Table: Economic dependency profile, by sex, taxfilers and income, and source of income | Variable: Canada Child Tax Benefit, Both sexes, Amount of income | Units: $CAD x 1,000, 2000-2011. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-137.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"Historical Architecture and Urbanism","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Census; Economic statistics; Official statistics; Social statistics; Population; Summary statistics; Social insurance; Social security; Population statistics; Government (linguistics)","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002870472,0.0007518467,0.001339748,0.0001114905,0.0003281297,0.0001160888,0.0009382783,0.0002908023,0.004919791],"category_scores_gemma":[0.000010103,0.0006987713,4.595762e-7,0.00006899062,0.0005705471,0.0001789123,0.0002473711,0.0006513505,0.000002438607],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005497143,"about_ca_system_score_gemma":0.01002714,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.999972,"about_ca_topic_score_gemma":0.999976,"domain_scores_codex":[0.9963741,0.0001994628,0.001025261,0.0009538812,0.0007858386,0.0006614534],"domain_scores_gemma":[0.9974418,0.0003321378,0.0007178936,0.0009872897,0.00004488523,0.0004760096],"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":[0.0001097514,0.00004587347,0.0003792665,0.0008012002,0.0003883952,0.00005543356,0.00007186624,0.000008031081,8.218666e-7,0.00163121,0.9963148,0.0001933856],"study_design_scores_gemma":[0.0009641247,0.0001208889,0.00006966991,0.00005691727,0.0002668396,0.00002978917,0.0001979953,0.0001302584,5.94552e-8,0.00000127295,0.9973903,0.0007718604],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00003504802,0.005013653,0.000002481864,0.000005913937,0.0005137594,0.0005529947,0.9873137,0.00002760516,0.006534829],"genre_scores_gemma":[0.0005242806,0.0005482281,0.00003465451,0.0002490362,0.0002337443,0.00002042079,0.9807733,0.0001867925,0.01742954],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01089471,"threshold_uncertainty_score":0.9995463,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01147743908308221,"score_gpt":0.1977672311490994,"score_spread":0.1862897920660172,"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."}}