{"id":"W6939159138","doi":"10.6068/dp14ba8b2ab8935","title":"Trend 1986 - 2011. Statistics Canada. CANSIM: Labor - Wages, Salaries and Other Earnings | Country: Canada | Table: Earnings of individuals, by selected characteristics and North American Industry Classification System (NAICS) | Variable: University degree, Business, building and other support services, Median earnings | Units: Constant 2011 $CAD $CAD, 1986-2011. 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; Census; Economic statistics; Socioeconomic status; Summary statistics; Official statistics; Wages and salaries; Publication","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":[0.001909981,0.00241397,0.002668445,0.008886126,0.003384375,0.004740118,0.005096065,0.001465673,0.08419087],"category_scores_gemma":[0.01532377,0.00167695,0.001849667,0.04132686,0.0005936077,0.00229017,0.002119031,0.003037127,0.05481574],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05155691,"about_ca_system_score_gemma":0.1225938,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9947245,"about_ca_topic_score_gemma":0.9932889,"domain_scores_codex":[0.9958514,0.0002410614,0.0003987118,0.0005145589,0.001957495,0.001036769],"domain_scores_gemma":[0.9684832,0.0010203,0.001056775,0.0008203203,0.0271609,0.001458598],"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":[0.00002381595,0.00000751262,0.001087876,0.0001973238,0.00001840142,0.000006477333,0.00002033179,0.0001132836,0.000008019351,0.0003680995,0.9965889,0.001559968],"study_design_scores_gemma":[0.0001612021,0.00001375632,0.03086468,0.0007796076,0.00006368311,0.00002641293,0.000497578,0.0005332823,0.0002090588,0.0006072089,0.9661523,0.00009130148],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006555781,0.00004960882,0.00002187776,0.0001074233,0.00002268471,0.00001279918,0.9988332,0.00005450922,0.0008323041],"genre_scores_gemma":[0.0008653579,0.0002686253,0.0003040034,0.0001204399,0.00001729491,0.00008959957,0.9937491,0.00008776882,0.004497858],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08419087,"threshold_uncertainty_score":0.3740733,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02055670342005322,"score_gpt":0.2176336170792194,"score_spread":0.1970769136591662,"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."}}