{"id":"W6920291321","doi":"10.6068/dp14ba8e4d99740","title":"Trend 1974 - 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 type of organization, type of plan and contributory status | Variable: Municipal government, public sector, Plans, Contributory registered pension plans, Number | Units: #, 1974-2012. 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; Personal income; Publication; 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":[0.001874233,0.002520547,0.003108744,0.008767854,0.003087642,0.005088834,0.005101519,0.001483122,0.08626159],"category_scores_gemma":[0.01878495,0.001863883,0.002174348,0.04363162,0.0006317553,0.002601466,0.002226733,0.003293365,0.06130122],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05021838,"about_ca_system_score_gemma":0.1209315,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9935873,"about_ca_topic_score_gemma":0.9921917,"domain_scores_codex":[0.9957311,0.0002390485,0.0004846725,0.000593966,0.002000738,0.0009505902],"domain_scores_gemma":[0.9648365,0.001299137,0.00118377,0.001079303,0.03011807,0.001483274],"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":[0.00002191274,0.000005932799,0.0009191621,0.0002066159,0.00002035837,0.000005451101,0.00001708781,0.00009109695,0.000007967314,0.000261778,0.9972998,0.001142719],"study_design_scores_gemma":[0.0001931918,0.00001177902,0.02266868,0.0007517862,0.00007271813,0.00002306823,0.0004142742,0.0004237261,0.000186588,0.0005618855,0.9746143,0.0000778844],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004627423,0.00003930135,0.00001469206,0.00008621641,0.00002055115,0.000009822349,0.9990919,0.00005066357,0.0006406269],"genre_scores_gemma":[0.0005867559,0.0001741512,0.000217221,0.00009716733,0.00001490894,0.00008580565,0.9956576,0.00007784403,0.003088519],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08626159,"threshold_uncertainty_score":0.3643615,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04955525156756628,"score_gpt":0.2765704900998738,"score_spread":0.2270152385323075,"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."}}