{"id":"W6920573889","doi":"10.6068/dp14ba8dc7b5668","title":"Trend 1997 - 2013. Statistics Canada. CANSIM: Labor - Wages, Salaries and Other Earnings | Country: Canada | Table: Labour force survey estimates (LFS), wages of employees by type of work, North American Industry Classification System (NAICS), sex and age group | Variable: 55 years and over, Finance, insurance, real estate and leasing, Males, Full-time, Median hourly wage rate | Units: Current $CAD, 1997-2013. 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; Census; Economic statistics; Wage; Wages and salaries; Summary statistics; Socioeconomic status; Official statistics; Immigration","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":[0.001942201,0.002476403,0.002904398,0.007568755,0.003034062,0.004634337,0.005147048,0.001486618,0.09447319],"category_scores_gemma":[0.0157635,0.001841742,0.002040593,0.03960977,0.0005676505,0.00227414,0.002212063,0.003265957,0.06196445],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04484713,"about_ca_system_score_gemma":0.1119008,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9939508,"about_ca_topic_score_gemma":0.9921507,"domain_scores_codex":[0.9960204,0.0002530599,0.0004228992,0.0004921367,0.001837968,0.0009734852],"domain_scores_gemma":[0.9678445,0.001087496,0.0009953368,0.0008312845,0.02773024,0.001511114],"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":[0.00002802858,0.000008344426,0.0009935057,0.00021548,0.00001970908,0.000005792255,0.00001845535,0.0000941946,0.000007673557,0.0002442239,0.9968195,0.001545085],"study_design_scores_gemma":[0.0002608947,0.00001916693,0.03612111,0.001058039,0.00008058803,0.00002927883,0.000576823,0.0005677321,0.0002004118,0.0006982469,0.9602861,0.0001015669],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005904472,0.00004397147,0.00001937192,0.0001000181,0.00002708474,0.00001455806,0.9989274,0.00005144955,0.000757054],"genre_scores_gemma":[0.000706802,0.000220175,0.0002623069,0.000142278,0.00001776804,0.0001065493,0.9945498,0.00008714358,0.003907151],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.09447319,"threshold_uncertainty_score":0.3253902,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02461106031344301,"score_gpt":0.2511244384278277,"score_spread":0.2265133781143847,"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."}}