{"id":"W6920723769","doi":"10.6068/dp14ba84f241f65","title":"Trend 2000 - 2011. Statistics Canada. CANSIM: Labor - Wages, Salaries and Other Earnings | Country: Canada | Table: Economic dependency profile, by sex, taxfilers and income, and source of income | Variable: Employment Insurance, Both sexes, Amount of income | Units: $CAD x 1,000, 2000-2011. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-145.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Earnings; Economic statistics; Census; Wages and salaries; Summary statistics; Socioeconomic status; Official statistics; Personal income; Immigration; Dependency ratio","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":[0.001981061,0.002474321,0.002631864,0.00803383,0.003216284,0.004871708,0.005376027,0.001563515,0.09478673],"category_scores_gemma":[0.01719521,0.001770055,0.002095107,0.03803988,0.0006354992,0.002525015,0.002312726,0.00310802,0.06130756],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04740058,"about_ca_system_score_gemma":0.1134198,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9937167,"about_ca_topic_score_gemma":0.9919937,"domain_scores_codex":[0.9961861,0.0002498109,0.0004020181,0.0005160341,0.001705608,0.0009405555],"domain_scores_gemma":[0.9706222,0.001091406,0.0009412083,0.0009491512,0.02498961,0.001406454],"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":[0.00002280288,0.000005684639,0.0008156635,0.0002063235,0.00001737999,0.000005927839,0.00001765225,0.00009623682,0.000007932673,0.0003183968,0.9970782,0.00140782],"study_design_scores_gemma":[0.0001611468,0.00001086115,0.02046421,0.0008582245,0.00006324956,0.00002547576,0.0003965189,0.000478763,0.0001783023,0.0007013594,0.9765708,0.00009095419],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004260454,0.00004323322,0.00002071605,0.0001035432,0.0000216473,0.00001121755,0.9989499,0.00005988565,0.0007472265],"genre_scores_gemma":[0.0006430727,0.0002274379,0.0003248134,0.0001304258,0.00001427242,0.00009451258,0.9951856,0.0001036989,0.003276296],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.09478673,"threshold_uncertainty_score":0.3439169,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01239823711147581,"score_gpt":0.224187509594833,"score_spread":0.2117892724833572,"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."}}