{"id":"W6901542110","doi":"10.6068/dp14ba8fe196f84","title":"Trend 1961 - 2010. Statistics Canada. CANSIM: Labor - Wages, Salaries and Other Earnings | Country: Canada | Table: Hours worked and labour compensation by type of worker and North American Industry Classification System (NAICS) | Variable: 15 to 34 years, University degrees or above, Hours worked, Paid workers, Religious, grant-making, civic, and professional and similar organizations, Males | Units: Hours x 1,000, 1961-2010. 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; Wages and salaries; Economic statistics; Socioeconomic status; Summary statistics; Official statistics; Immigration; Compensation (psychology)","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":[0.001651659,0.002493717,0.002738658,0.007809797,0.002818064,0.004148479,0.005296411,0.001450903,0.0745585],"category_scores_gemma":[0.01345935,0.001698866,0.001909585,0.0391022,0.0005990349,0.0020444,0.001932637,0.003211753,0.05245754],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04433151,"about_ca_system_score_gemma":0.1000944,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9937109,"about_ca_topic_score_gemma":0.992059,"domain_scores_codex":[0.996483,0.0001933961,0.0003511769,0.0004867811,0.001622968,0.0008628336],"domain_scores_gemma":[0.9724094,0.0009144767,0.001019984,0.0007256051,0.02364617,0.0012843],"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":[0.00002671556,0.000008948978,0.001298059,0.0002025788,0.00002142712,0.000005963862,0.00001748292,0.0001215856,0.000008657307,0.0002465731,0.9967482,0.001293857],"study_design_scores_gemma":[0.0002555258,0.0000184466,0.04414172,0.0008819459,0.00007716569,0.00002964546,0.000535997,0.000639975,0.0002412664,0.0006124651,0.952465,0.000100792],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006446432,0.00003988653,0.0000153637,0.00008041679,0.00002257552,0.000009773637,0.9991812,0.00004458623,0.0005417109],"genre_scores_gemma":[0.0006891037,0.0001732535,0.0001871244,0.0000962215,0.0000157261,0.00006865585,0.9957725,0.00006051195,0.002936952],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0745585,"threshold_uncertainty_score":0.3216491,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02380301184331624,"score_gpt":0.2394554701445875,"score_spread":0.2156524583012712,"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."}}