{"id":"W6977514116","doi":"10.6068/dp14ba8d2ab4e36","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 for all jobs, Finance, insurance, real estate and renting and leasing, Business sector | Units: $CAD x 1,000, 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; Wages and salaries; Census; Public sector; National accounts; Social statistics; Descriptive statistics","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":[0.001927247,0.00227171,0.00271198,0.008425844,0.003301793,0.004734877,0.004834807,0.001433337,0.08184055],"category_scores_gemma":[0.01677148,0.001694281,0.001949363,0.0401087,0.0006293071,0.002441572,0.002262386,0.003098489,0.05478762],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05182195,"about_ca_system_score_gemma":0.1331518,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9947152,"about_ca_topic_score_gemma":0.9935156,"domain_scores_codex":[0.9958156,0.0002706114,0.0004358948,0.0005417806,0.00196821,0.0009678997],"domain_scores_gemma":[0.9650373,0.001095907,0.0009739959,0.0009188452,0.0304616,0.001512397],"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":[0.00002150623,0.000006294447,0.0009078339,0.0002074252,0.00001959171,0.00000669135,0.00001964955,0.00009292176,0.000007827189,0.0003332919,0.9969898,0.001387156],"study_design_scores_gemma":[0.000143572,0.00001235492,0.02522293,0.0007924027,0.00007015526,0.00002925215,0.0004933272,0.0004812795,0.0001792072,0.000627052,0.9718625,0.00008596751],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005936134,0.00005149372,0.00002109939,0.0001206334,0.00002780287,0.00001269515,0.9988348,0.00005375226,0.0008183104],"genre_scores_gemma":[0.0007782826,0.0002603105,0.0003014542,0.0001461717,0.00001813715,0.00009320145,0.9943846,0.00009159773,0.003926163],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08184055,"threshold_uncertainty_score":0.3759964,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02618587517269361,"score_gpt":0.2366080649980019,"score_spread":0.2104221898253082,"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."}}