{"id":"W6977028804","doi":"10.6068/dp14ba8b8bcb384","title":"Trend 1983 - 2000. Statistics Canada. CANSIM: Labor - Wages, Salaries and Other Earnings | Country: Canada | Table: Average hourly earnings and average weekly hours of employees paid by the hour (SEPH) | Variable: Average hourly earnings, Including overtime, Automotive parts and accessories stores | Units: , 1983-2000. 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; Summary statistics; Wages and salaries; Official statistics; Socioeconomic status; Personal income; Statistical analysis","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":[0.001753204,0.002444753,0.002540879,0.008114348,0.003062826,0.004674769,0.004998365,0.001433828,0.0792954],"category_scores_gemma":[0.01395918,0.001648487,0.00178219,0.04034755,0.000595114,0.00224577,0.002002332,0.002982408,0.05490658],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04654577,"about_ca_system_score_gemma":0.1109111,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.994672,"about_ca_topic_score_gemma":0.9927157,"domain_scores_codex":[0.9963077,0.0002179822,0.0003387247,0.0004782696,0.001714745,0.0009424681],"domain_scores_gemma":[0.9732474,0.0008885253,0.0008787514,0.0007633512,0.02300018,0.001221824],"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":[0.00002373015,0.000007392991,0.001039927,0.0001893843,0.00001819485,0.000006832042,0.00001981628,0.0001250834,0.000008486881,0.0003430382,0.9966578,0.001560201],"study_design_scores_gemma":[0.0001649963,0.00001308689,0.0287484,0.0007431718,0.00005875427,0.00002475862,0.0004835597,0.0005792908,0.0002115891,0.0006655263,0.968216,0.00009074146],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005793641,0.00004134452,0.00002179483,0.00009504657,0.00002081901,0.00001179597,0.9989241,0.00005959985,0.0007676064],"genre_scores_gemma":[0.0008526251,0.0002251999,0.0003063043,0.0001103825,0.00001531581,0.00008734385,0.9945462,0.00009241844,0.003764162],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0792954,"threshold_uncertainty_score":0.3377148,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02104076349664799,"score_gpt":0.245662660612387,"score_spread":0.224621897115739,"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."}}