{"id":"W6976856036","doi":"10.6068/dp14ba8c8d48290","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, Other federal government services and defence services, Business sector | Units: , 1997-2011. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-104.","year":2015,"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; Wages and salaries; Public sector; Social statistics; Private sector; National accounts","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":[0.002246893,0.002411615,0.002725934,0.008868496,0.003579952,0.005062399,0.004866173,0.001498671,0.09089907],"category_scores_gemma":[0.01876187,0.001818817,0.002000652,0.04147851,0.0006264204,0.002688851,0.002346056,0.003276069,0.05958361],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05322346,"about_ca_system_score_gemma":0.1418173,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9940453,"about_ca_topic_score_gemma":0.9921401,"domain_scores_codex":[0.9952652,0.0003169888,0.0004908823,0.0005815999,0.002283957,0.001061375],"domain_scores_gemma":[0.9616919,0.001220183,0.001025832,0.0010298,0.03345759,0.001574622],"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":[0.0000206738,0.000006121353,0.000810173,0.0002088416,0.00001850572,0.000006662039,0.00001926159,0.00008921482,0.000007840941,0.0003520992,0.9969126,0.001547972],"study_design_scores_gemma":[0.0001276042,0.00001110376,0.02108759,0.0007995279,0.00006357278,0.00002746603,0.000456212,0.0004044205,0.0001671908,0.0006383861,0.976133,0.00008373053],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005424968,0.00005565834,0.00002405564,0.000140157,0.00003380043,0.00001444186,0.9986833,0.00005742195,0.0009369488],"genre_scores_gemma":[0.0007848869,0.0003054416,0.0003717597,0.0001705884,0.00002148974,0.0001108632,0.9934216,0.0001177648,0.004695695],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.09089907,"threshold_uncertainty_score":0.386165,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01962698542065709,"score_gpt":0.221080185685416,"score_spread":0.2014532002647589,"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."}}