{"id":"W6939263384","doi":"10.6068/dp14ba8e6de2955","title":"Trend 1974 - 2006. Statistics Canada. CANSIM: Income, Pensions, Spending and Wealth - Pensions Plans and Funds and Other Retirement Income Programs | Country: Canada | Table: Registered pension plans (RPPs) and members, by class of employees eligible for the plan, sector, type of plan and contributory status | Variable: Total class of employees, Members, females, Private sector registered pension plans, Number | Units: #, 1974-2006. 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; Socioeconomic status; Population; 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":[0.001843391,0.002497146,0.002876628,0.008070163,0.00294719,0.004593654,0.004729161,0.001310996,0.08697327],"category_scores_gemma":[0.01679338,0.001851399,0.001935458,0.04071747,0.0005951736,0.002414218,0.00199585,0.003192313,0.06264023],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04711117,"about_ca_system_score_gemma":0.1119474,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9929415,"about_ca_topic_score_gemma":0.9914966,"domain_scores_codex":[0.9960526,0.0002352352,0.0004365091,0.0005490957,0.001858946,0.0008676207],"domain_scores_gemma":[0.9685148,0.001165973,0.001071486,0.0009834686,0.02691144,0.00135295],"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":[0.00002325076,0.000006389144,0.0009760155,0.0001911878,0.00001843301,0.000005331075,0.0000169622,0.00009110971,0.000008468011,0.0002535023,0.9970933,0.001316054],"study_design_scores_gemma":[0.0001709527,0.00001216319,0.02325095,0.0006627049,0.00006154932,0.00002196354,0.0003660236,0.0004073884,0.0001747789,0.0005307418,0.9742702,0.00007045212],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004674082,0.00003679827,0.00001561883,0.00007419665,0.00002026981,0.000009861828,0.9991053,0.00005144831,0.0006397072],"genre_scores_gemma":[0.0005284466,0.0001701211,0.0002228312,0.00008630004,0.00001380748,0.00007808256,0.9957118,0.00007551661,0.003113223],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08697327,"threshold_uncertainty_score":0.3418171,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06852267816775379,"score_gpt":0.2932835746815777,"score_spread":0.2247608965138239,"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."}}