{"id":"W6938701800","doi":"10.6068/dp14ba8c8c29885","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: Paper manufacturing, Plans, Total of registered pension plans, Number | 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; Official statistics; Population; 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":[0.001680251,0.002530648,0.003066962,0.007654218,0.002841746,0.004870788,0.005375918,0.001605612,0.08053426],"category_scores_gemma":[0.01692226,0.001671664,0.002237114,0.0380886,0.0005778967,0.002438707,0.002143483,0.00318116,0.06354487],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03766268,"about_ca_system_score_gemma":0.0866375,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9878954,"about_ca_topic_score_gemma":0.9872832,"domain_scores_codex":[0.9965504,0.0002175636,0.0004042226,0.0005483508,0.001510263,0.0007690817],"domain_scores_gemma":[0.9727604,0.001113527,0.0009471623,0.00100836,0.02296095,0.001209628],"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":[0.00002291407,0.000005493237,0.0007951244,0.000194756,0.00001984398,0.000005102259,0.00001266828,0.00007918841,0.000006934441,0.0002152632,0.9976873,0.0009553385],"study_design_scores_gemma":[0.0002323713,0.00001200142,0.02061222,0.0008154394,0.00007883305,0.00002613456,0.0003612303,0.0004819581,0.0001725877,0.0006200993,0.9765092,0.00007801403],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00003696064,0.00003445104,0.0000108658,0.00007096792,0.00001804361,0.000007283405,0.9993299,0.00004024195,0.0004513439],"genre_scores_gemma":[0.0003775828,0.0001204858,0.0001352283,0.00007386649,0.0000117716,0.00006056861,0.9974789,0.00004972355,0.001691855],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08053426,"threshold_uncertainty_score":0.2732632,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04397669256682908,"score_gpt":0.2723657460234105,"score_spread":0.2283890534565814,"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."}}