{"id":"W6920252828","doi":"10.6068/dp14ba8efbf626","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 employer contribution rate, sector, type of plan and contributory status | Variable: Members, males, Employer contributions based on other rate, Total of 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; Descriptive statistics; Census; Social security; Population; Official statistics; Socioeconomic status; 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":[0.00173378,0.002564602,0.00304429,0.007902258,0.002969248,0.004937622,0.005034133,0.001477012,0.0803067],"category_scores_gemma":[0.01627134,0.001787197,0.002107688,0.03980719,0.000613898,0.002456483,0.002137641,0.003329809,0.06091135],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04355237,"about_ca_system_score_gemma":0.1002814,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9917538,"about_ca_topic_score_gemma":0.9906946,"domain_scores_codex":[0.9964321,0.0002126867,0.0004060031,0.0005449029,0.00158988,0.0008144216],"domain_scores_gemma":[0.9723254,0.001049016,0.0009865935,0.0009552886,0.02343084,0.001252896],"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":[0.00002354556,0.000006279333,0.0009768264,0.0001977867,0.00002102865,0.000005531613,0.00001620477,0.00009178807,0.000008576506,0.0002370265,0.9972534,0.001162014],"study_design_scores_gemma":[0.0002140972,0.00001253234,0.02359461,0.0007882193,0.00007360863,0.00002463914,0.0004047425,0.0004623737,0.0001863523,0.0005956614,0.9735625,0.00008064168],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0000440104,0.00003713034,0.00001346747,0.000072963,0.00001871186,0.000008540052,0.9992493,0.00004770113,0.000508218],"genre_scores_gemma":[0.0004736706,0.0001461262,0.0001802173,0.0000832548,0.00001298624,0.00007122668,0.996671,0.00006344125,0.002298105],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0803067,"threshold_uncertainty_score":0.3159961,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04095312700940171,"score_gpt":0.2842526290376305,"score_spread":0.2432995020282288,"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."}}