{"id":"W6939424553","doi":"10.6068/dp14ba8ca3beb38","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: 15 to 24 years, Total employees, all industries, Males, Part-time, Total employees, all wages | Units: Current $CAD Persons x 1,000, 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; Wages and salaries; Census; Economic statistics; Summary statistics; Socioeconomic status; Official statistics; Immigration; Wage","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":[0.002046235,0.002441491,0.002996777,0.007552003,0.003015369,0.004433576,0.005245284,0.001421517,0.08793046],"category_scores_gemma":[0.01587817,0.001779995,0.002068149,0.03913773,0.0005607231,0.002194363,0.002191489,0.003288827,0.05734348],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04513201,"about_ca_system_score_gemma":0.1136355,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9941209,"about_ca_topic_score_gemma":0.9923059,"domain_scores_codex":[0.9960756,0.0002599549,0.0004263374,0.0004922551,0.00179273,0.0009532],"domain_scores_gemma":[0.9688669,0.001043273,0.0009620226,0.0008327459,0.0268444,0.00145062],"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":[0.00002883426,0.000008087969,0.001034594,0.0002290324,0.00002230856,0.000005872294,0.00001913552,0.00009374521,0.00000767334,0.0002567339,0.9965927,0.00170136],"study_design_scores_gemma":[0.0002482575,0.00001884796,0.03747763,0.001080097,0.00009161977,0.00003074505,0.0005702169,0.0005624833,0.0001930231,0.0007220631,0.9589016,0.0001034138],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006237083,0.00005142869,0.00002164032,0.0001096868,0.00002961511,0.00001475197,0.9989214,0.00005381832,0.0007352659],"genre_scores_gemma":[0.0007468787,0.0002497498,0.0002881143,0.0001516293,0.00001861812,0.000111963,0.9943945,0.00008848528,0.003949962],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08793046,"threshold_uncertainty_score":0.3274572,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04207290194912602,"score_gpt":0.2645677563256469,"score_spread":0.2224948543765209,"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."}}