{"id":"W2172128844","doi":"10.25336/p67k5x","title":"The Role of Microsimulation in Longitudinal Data Analysis","year":2001,"lang":"en","type":"article","venue":"Canadian Studies in Population","topic":"demographic modeling and climate adaptation","field":"Decision Sciences","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Microsimulation; Imputation (statistics); Econometrics; Longitudinal data; Computer science; Range (aeronautics); Missing data; Economics; Data mining; Transport engineering; Engineering; Machine learning","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002608854,0.00007039805,0.0002126318,0.0009935237,0.0001620116,0.00004746187,0.0004099829,0.00004258087,0.00001044538],"category_scores_gemma":[0.00153059,0.000051806,0.00004303883,0.003214704,0.00007134867,0.0002460826,0.00005589799,0.00006429984,0.000004315305],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001262171,"about_ca_system_score_gemma":0.0000384801,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1255098,"about_ca_topic_score_gemma":0.942296,"domain_scores_codex":[0.9983271,0.0001264583,0.0006571052,0.0003087315,0.0003868736,0.0001936821],"domain_scores_gemma":[0.9984232,0.0005343712,0.0001806515,0.0006344752,0.0001804876,0.0000468097],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000008715247,0.000004772876,0.9326397,6.249387e-7,0.0000307399,0.000001648447,0.0003856599,0.03439369,0.000003321078,0.0008206766,0.0000464958,0.03166392],"study_design_scores_gemma":[0.00007797751,0.000004208556,0.759498,0.000007280375,0.00002621011,4.385513e-7,0.002678168,0.2183659,6.300601e-7,0.0184751,0.0008218036,0.00004426556],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9960571,0.002445568,0.0003220393,0.0002772894,0.0001436631,0.0001035001,0.00002099175,0.000004255535,0.0006255884],"genre_scores_gemma":[0.9994102,0.0003022481,0.000123752,0.0000177921,0.00001785402,0.000004349675,0.0000746412,0.00000324007,0.00004590155],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8167862,"threshold_uncertainty_score":0.8803135,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.320375833259936,"score_gpt":0.4642547693894598,"score_spread":0.1438789361295237,"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."}}