{"id":"W6957640524","doi":"10.6068/dp14ba8f58ae960","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: 55 years and over, University degrees or above, Labour compensation, Paid workers, Educational services (except universities), Males | Units: , 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; Summary statistics; Socioeconomic status; 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":[0.001811098,0.002436325,0.002722223,0.007895861,0.002970322,0.004506581,0.005203308,0.001470373,0.08042111],"category_scores_gemma":[0.01485734,0.001659417,0.001883076,0.03905931,0.0006181747,0.00224927,0.002017061,0.003155896,0.05642893],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04675231,"about_ca_system_score_gemma":0.1131053,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.993768,"about_ca_topic_score_gemma":0.9919904,"domain_scores_codex":[0.9960147,0.0002298268,0.0003862947,0.0005466734,0.001835514,0.0009870894],"domain_scores_gemma":[0.9692695,0.001067714,0.001107213,0.0008338923,0.02635276,0.001368897],"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":[0.00002479301,0.000007838937,0.001163142,0.0001853741,0.00001947311,0.000005847952,0.00001762259,0.000109449,0.000008634071,0.0002775271,0.9968783,0.001302037],"study_design_scores_gemma":[0.0002023419,0.00001518023,0.03252508,0.0007668581,0.00006581972,0.00002679709,0.0004849996,0.000516841,0.0002243613,0.0005905217,0.964487,0.00009428518],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006357716,0.00004517114,0.00001793418,0.00009927226,0.00002462906,0.00001078557,0.9990081,0.00005014329,0.0006803994],"genre_scores_gemma":[0.0007608193,0.0002026423,0.0002358212,0.0001209863,0.00001740465,0.000076808,0.994751,0.00007801123,0.003756369],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08042111,"threshold_uncertainty_score":0.3392134,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02370894226766192,"score_gpt":0.2324932833553245,"score_spread":0.2087843410876625,"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."}}