{"id":"W7099562164","doi":"","title":"i How Do Public Pensions Affect Retirement Incomes and Expenditures? Evidence over Five Decades from Canada","year":2014,"lang":"en","type":"article","venue":"","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Microdata (statistics); Counterfactual thinking; Pension; Affect (linguistics); Public use; Agency (philosophy); Pension system; Work (physics)","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":[0.002214326,0.0003736005,0.0007294287,0.002580744,0.002476654,0.001511143,0.00150377,0.0006885644,0.003001392],"category_scores_gemma":[0.007917907,0.0003246948,0.001627799,0.00778596,0.000822473,0.0005195824,0.001087293,0.001093888,0.0003251],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.023056,"about_ca_system_score_gemma":0.03130261,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9969103,"about_ca_topic_score_gemma":0.9974367,"domain_scores_codex":[0.9984675,0.0002155766,0.0001359304,0.000240378,0.0004897023,0.00045086],"domain_scores_gemma":[0.9890217,0.001714333,0.002407192,0.0007222278,0.004832303,0.001302296],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000406823,0.00006129632,0.9614131,0.0002534186,0.0008341075,0.0001562573,0.0008679014,0.001513819,0.00009820735,0.001965076,0.01373673,0.01869323],"study_design_scores_gemma":[0.00004357856,0.00003082998,0.9874094,0.0002620588,0.0006341257,0.00004158855,0.001145048,0.000904694,0.0001428385,0.000206042,0.00914692,0.00003301438],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8747519,0.03236553,0.0004825406,0.01105459,0.0001318751,0.00005979009,0.06664595,0.00005469281,0.01445312],"genre_scores_gemma":[0.9681438,0.01025689,0.0003123216,0.0005840859,0.00004237868,0.00002076133,0.01771913,0.0000149965,0.00290559],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.023056,"threshold_uncertainty_score":0.1672838,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01834444234050768,"score_gpt":0.2409336564181283,"score_spread":0.2225892140776206,"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."}}