{"id":"W6920194199","doi":"10.6068/dp14ba8b4c7c159","title":"Trend 1986 - 2011. Statistics Canada. CANSIM: Labor - Wages, Salaries and Other Earnings | Country: Canada | Table: Earnings of individuals, by selected characteristics and National Occupational Classification | Variable: 45 to 54 years, Business, finance and administrative occupations, Average earnings | Units: # Persons $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; Wages and salaries; Official statistics; National accounts; 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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001975464,0.002356108,0.002688146,0.008480427,0.003337456,0.004717012,0.005037743,0.001482752,0.08623677],"category_scores_gemma":[0.01531587,0.001723959,0.001865472,0.039986,0.0005905463,0.002346527,0.002084052,0.003164914,0.05358851],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05198107,"about_ca_system_score_gemma":0.1270512,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.9950637,"about_ca_topic_score_gemma":0.9934939,"domain_scores_codex":[0.995679,0.0002586326,0.0004327337,0.0005037361,0.002066766,0.0010591],"domain_scores_gemma":[0.9694725,0.0009528864,0.001001463,0.0007489304,0.02642031,0.001403972],"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":[0.00002573796,0.000007928617,0.001132953,0.000226537,0.00001954112,0.000007139286,0.00002128537,0.0001103961,0.000008125523,0.0003749876,0.9963898,0.001675593],"study_design_scores_gemma":[0.0001683837,0.00001464184,0.03312146,0.0008818595,0.00006635724,0.00002890795,0.0005030366,0.0004946824,0.0001900771,0.0006321854,0.9638034,0.00009505354],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006558168,0.00005851888,0.00002369799,0.0001225488,0.00002677407,0.00001482858,0.9987072,0.00005316373,0.0009277458],"genre_scores_gemma":[0.001003651,0.0003404775,0.0003507236,0.0001498244,0.00002074077,0.0001073647,0.9928455,0.00009721815,0.005084536],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9950637,"threshold_uncertainty_score":0.3771509,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02901719168924979,"score_gpt":0.2623794807273854,"score_spread":0.2333622890381356,"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."}}