{"id":"W6901533663","doi":"10.6068/dp14ba8e3b8fd24","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: All trade industries, Members, both sexes, Defined contribution registered pension plans | 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; Socioeconomic status; Population; 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":[0.001768624,0.00248771,0.003115847,0.007672022,0.002962577,0.004891938,0.005311503,0.001497967,0.07572359],"category_scores_gemma":[0.01669961,0.001747321,0.002217964,0.03866817,0.0005882762,0.002452092,0.002223219,0.003222567,0.05819757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03969638,"about_ca_system_score_gemma":0.09573529,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9900723,"about_ca_topic_score_gemma":0.9892257,"domain_scores_codex":[0.9964855,0.0002225429,0.0004180702,0.00054472,0.00153744,0.0007918637],"domain_scores_gemma":[0.971595,0.001075003,0.0009933194,0.000990612,0.02407721,0.001268866],"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":[0.00002674477,0.000006226739,0.0009857118,0.0002179062,0.00002338471,0.000005820554,0.00001680851,0.00008975483,0.000007991373,0.0002539461,0.9972385,0.001127217],"study_design_scores_gemma":[0.0002289783,0.00001296686,0.02444042,0.0008895378,0.00008624001,0.00002803239,0.0004285959,0.0004857156,0.0001909742,0.0006546826,0.9724677,0.00008627152],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004399657,0.00003790051,0.00001260359,0.00007818026,0.0000194841,0.000008073935,0.9992717,0.00004118926,0.000486857],"genre_scores_gemma":[0.0004570052,0.000144749,0.0001598411,0.0000831885,0.00001267481,0.00006795912,0.9970187,0.00005599128,0.001999834],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07572359,"threshold_uncertainty_score":0.2880188,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05791862894904772,"score_gpt":0.2745315643110767,"score_spread":0.216612935362029,"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."}}