{"id":"W1968211333","doi":"10.1002/atr.138","title":"Intelligent transportation systems: an impact analysis for Michigan","year":2010,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Environmental Impact and Sustainability","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Michigan State University","keywords":"Economic impact analysis; Work (physics); Computable general equilibrium; Macro; Economic analysis; Operations research; Transportation industry; Intelligent transportation system; Transport engineering; Input–output model; Economics; Computer science; Environmental economics; Engineering; Agricultural economics; Macroeconomics; Microeconomics","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":[0.0004176922,0.0001527085,0.0002849805,0.0001155032,0.00008069885,0.00002500646,0.0001553163,0.00007972359,0.0002909755],"category_scores_gemma":[0.00001964737,0.0001249241,0.0003545887,0.0003291387,0.00007887948,0.001047834,7.821462e-7,0.0001961665,0.000002113566],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001129309,"about_ca_system_score_gemma":0.00001940368,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000135643,"about_ca_topic_score_gemma":0.002897108,"domain_scores_codex":[0.9986341,0.00002656603,0.0006323943,0.0001796264,0.0003213815,0.0002059414],"domain_scores_gemma":[0.9990839,0.00004180078,0.0004396353,0.0001732505,0.00004647883,0.0002149518],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0003099601,0.0002208222,0.3667079,0.000019004,0.0001262863,0.000005222299,0.003444146,0.5523674,0.07386498,0.00004714416,0.000005872326,0.002881238],"study_design_scores_gemma":[0.0005180963,0.000525339,0.9910879,0.00000502757,0.000422599,0.000002962537,0.002044212,0.0006838493,0.003448955,0.0004389986,0.0006753732,0.0001466559],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.965275,0.00002404649,0.03401328,0.00003324315,0.0002472766,0.0003145349,0.00006395161,0.00001101036,0.00001769504],"genre_scores_gemma":[0.9929678,0.00002041211,0.006725959,0.0000198414,0.00005682225,0.00001034221,0.0001535758,0.00001520196,0.00003000426],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.62438,"threshold_uncertainty_score":0.5094256,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00963841992871615,"score_gpt":0.2898234495717112,"score_spread":0.2801850296429951,"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."}}