{"id":"W6958637986","doi":"10.6068/dp14ba8e5c18534","title":"Trend 1999 - 2012. Statistics Canada. CANSIM: Income, Pensions, Spending and Wealth - Pensions Plans and Funds and Other Retirement Income Programs | Country: Canada | Table: Registered pension plans (RPPs), members and market value of assets, by North American Industry Classification System (NAICS), sector, type of plan and contributory status | Variable: Public administration, Members, both sexes, Public sector registered pension plans, Number | Units: #, 1999-2012. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-122.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Pension; Census; Descriptive statistics; Social security; Population; Socioeconomic status; Official statistics; Publication; Personal income; Summary 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":[0.001838785,0.002489398,0.003160806,0.00776307,0.003018522,0.004943011,0.005387564,0.001500264,0.07636203],"category_scores_gemma":[0.0172863,0.001759277,0.002218215,0.03931411,0.000592009,0.002480689,0.002209931,0.003272946,0.05866873],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0410684,"about_ca_system_score_gemma":0.1013274,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9905094,"about_ca_topic_score_gemma":0.9895891,"domain_scores_codex":[0.9963101,0.0002344981,0.0004366327,0.0005589104,0.001643925,0.0008160688],"domain_scores_gemma":[0.9700671,0.00110579,0.001022119,0.001030474,0.02544642,0.001328003],"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":[0.00002668328,0.000006196608,0.0009737697,0.0002102638,0.00002276263,0.0000057035,0.00001675217,0.00008826819,0.000007746256,0.0002536942,0.9972497,0.001138537],"study_design_scores_gemma":[0.0002269836,0.000013192,0.02437192,0.0008658362,0.0000864558,0.00002742603,0.0004366907,0.0004888398,0.000186694,0.0006575658,0.9725524,0.00008598817],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004602169,0.00003913297,0.00001342927,0.00008219259,0.00002081702,0.000008769749,0.9992288,0.00004357267,0.0005171701],"genre_scores_gemma":[0.0004742592,0.0001482531,0.0001693207,0.00008663251,0.0000133285,0.00007147853,0.9968643,0.00005852649,0.002113833],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07636203,"threshold_uncertainty_score":0.2979735,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06532760903365809,"score_gpt":0.2844839982857387,"score_spread":0.2191563892520806,"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."}}