{"id":"W6938907976","doi":"10.6068/dp14ba8f1629376","title":"Trend 1980 - 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 method of automatic adjustment of pension for defined pension plans, sector, and contributory status | Variable: Other method of adjustment, Members, both sexes, Public sector defined benefit registered pension plans | Units: # %, 1980-2012. 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; Population; Socioeconomic status; Official statistics; Publication; Social security; 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":[0.001686911,0.002498749,0.003124075,0.008066218,0.002956424,0.004613133,0.005192473,0.001508561,0.07257989],"category_scores_gemma":[0.0163171,0.001811618,0.002110484,0.03938183,0.0006307311,0.002394953,0.002165737,0.003243696,0.05762535],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04243987,"about_ca_system_score_gemma":0.1003585,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.991418,"about_ca_topic_score_gemma":0.9909973,"domain_scores_codex":[0.9965178,0.0002020641,0.0004124007,0.0005623029,0.001508646,0.000796706],"domain_scores_gemma":[0.9714195,0.001054214,0.001031533,0.001004397,0.02419869,0.001291691],"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":[0.00002637397,0.000006156556,0.001031134,0.0002283601,0.00002330364,0.000005468026,0.00001783873,0.00008670829,0.000008798866,0.0002375867,0.9972131,0.001115194],"study_design_scores_gemma":[0.0002267103,0.00001249293,0.02718158,0.0008464961,0.0000868688,0.00002725062,0.0004136674,0.0004327635,0.0002036265,0.0005656127,0.9699164,0.00008647559],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0000472262,0.00003852825,0.00001219117,0.00006850029,0.00001865654,0.000007823415,0.9992918,0.00004640326,0.0004688477],"genre_scores_gemma":[0.0004905136,0.0001383893,0.0001548528,0.0000758859,0.00001271369,0.00006823437,0.9969109,0.00005909695,0.002089584],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07257989,"threshold_uncertainty_score":0.3079243,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05419005911761304,"score_gpt":0.296789208127192,"score_spread":0.2425991490095789,"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."}}