{"id":"W2163671928","doi":"10.1177/0894439310370084","title":"Highlights of Contemporary Microsimulation","year":2010,"lang":"en","type":"article","venue":"Social Science Computer Review","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Statistics Canada","funders":"","keywords":"Microsimulation; Macro; Field (mathematics); Macro level; Economics; Regional science; Computer science; Public economics; Operations research; Econometrics; Sociology; Transport engineering; Macroeconomics; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002253997,0.00004448402,0.0001048996,0.00003826465,0.0000722454,0.00001078933,0.0001346062,0.00002002473,0.00002652985],"category_scores_gemma":[0.000004626318,0.00004079513,0.0000347616,0.0005348637,0.0001483199,0.0001549409,0.00000624309,0.00006480171,0.000008003807],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008148318,"about_ca_system_score_gemma":0.00004411224,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002787514,"about_ca_topic_score_gemma":0.000003670719,"domain_scores_codex":[0.9995254,0.000004385848,0.0002003454,0.00007628123,0.0001218247,0.00007175833],"domain_scores_gemma":[0.9997413,0.000008546322,0.00003259182,0.00008303278,0.0001103488,0.0000241733],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000001918303,0.000133516,0.002978303,0.00350677,0.00002965405,0.000001714802,0.002421218,0.0002687017,0.2393062,0.4805827,0.01611912,0.2546502],"study_design_scores_gemma":[0.0005382076,0.0000400747,0.2587314,0.0009544331,0.0000382296,0.000003106572,0.00001484933,0.007637054,0.01272823,0.001308991,0.7174231,0.0005823095],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7954101,0.0109971,0.1252612,0.005120549,0.008275085,0.002701514,0.00007108007,0.001303117,0.05086025],"genre_scores_gemma":[0.9979025,0.0001915834,0.001609793,0.000215229,0.00006158968,0.000004163923,0.000006994067,0.000003085186,0.000005135758],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.701304,"threshold_uncertainty_score":0.1663577,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02001432706340182,"score_gpt":0.2845717772646054,"score_spread":0.2645574502012035,"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."}}