{"id":"W6901654714","doi":"10.6068/dp15063086f3914","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, Trade, Males, Full-time, Median weekly 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; Wages and salaries; Wage; 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":[0.002133392,0.00252973,0.002971884,0.007782811,0.002908575,0.004603105,0.005523944,0.001533474,0.09255693],"category_scores_gemma":[0.01707406,0.001878726,0.002120894,0.0400629,0.0005842143,0.002298472,0.002178952,0.003363865,0.05915401],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04645446,"about_ca_system_score_gemma":0.1170694,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9937967,"about_ca_topic_score_gemma":0.9918989,"domain_scores_codex":[0.9958153,0.0002772751,0.0004553805,0.0005262027,0.001918091,0.001007633],"domain_scores_gemma":[0.9661142,0.001208393,0.001103588,0.0008937331,0.02910002,0.001580123],"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":[0.00002826862,0.000007966637,0.0009779065,0.0002288173,0.00002135537,0.000005568678,0.00001746175,0.00009818134,0.000007688243,0.0002507995,0.9968575,0.001498489],"study_design_scores_gemma":[0.0002844921,0.00001831979,0.03367034,0.001104388,0.00008763746,0.0000292365,0.0005142577,0.0005545132,0.0002020952,0.0007381053,0.9626921,0.0001044979],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005459705,0.00004427501,0.00001981625,0.0001027636,0.00002659742,0.00001485812,0.9989641,0.00005362572,0.0007194433],"genre_scores_gemma":[0.000713361,0.0002295341,0.0002920592,0.0001551576,0.0000183505,0.0001159813,0.994679,0.00009539176,0.003701164],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.09255693,"threshold_uncertainty_score":0.3370523,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02873174080797221,"score_gpt":0.2535833999987438,"score_spread":0.2248516591907716,"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."}}