{"id":"W6938884366","doi":"10.6068/dp157abe54e9649","title":"Trend 1997 - 2011. Statistics Canada. CANSIM: Government - Employment and Remuneration | Country: Canada | Table: Labour statistics by business sector industry and non-commercial activity, consistent with the System of National Accounts, by North American Industry Classification System (NAICS) | Variable: Total compensation per job, Non-profit welfare organizations, Business sector | Units: , 1997-2011. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-104.","year":2016,"lang":"en","type":"other","venue":"Data Planet","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Economic statistics; Official statistics; Government (linguistics); Remuneration; Census; Public sector; Social statistics; Wages and salaries; National accounts; Business sector","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":[0.002145751,0.002401281,0.002658413,0.008565508,0.003399055,0.004982461,0.00498085,0.001489924,0.08628554],"category_scores_gemma":[0.01848559,0.001789228,0.001953165,0.04023438,0.0006233928,0.002682797,0.002335351,0.003315059,0.06063797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.048741,"about_ca_system_score_gemma":0.128621,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9932347,"about_ca_topic_score_gemma":0.9914152,"domain_scores_codex":[0.9956093,0.0002920253,0.0004527546,0.0005620789,0.002089596,0.0009941504],"domain_scores_gemma":[0.9644936,0.001192834,0.0009664312,0.001041815,0.03082086,0.001484503],"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":[0.00001995533,0.000005806496,0.0007941321,0.0001958829,0.00001747495,0.000006348735,0.00001800645,0.00008463333,0.000007847645,0.0003193736,0.9971219,0.001408711],"study_design_scores_gemma":[0.0001351994,0.00001063994,0.02020935,0.0007688801,0.00006072334,0.00002717806,0.0004354628,0.0004152113,0.0001736132,0.0006547121,0.9770281,0.00008086748],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004836535,0.0000439118,0.00002112168,0.0001158199,0.00002818181,0.0000123646,0.9989008,0.00005322736,0.0007762911],"genre_scores_gemma":[0.0006324314,0.0002353258,0.0003093123,0.0001333222,0.00001787488,0.00009514672,0.9948079,0.0001040451,0.003664747],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08628554,"threshold_uncertainty_score":0.3536423,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01540615519375416,"score_gpt":0.2213535442923082,"score_spread":0.205947389098554,"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."}}