{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005403864,0.0004127912,0.0002037951,0.00130531,0.0004263605,0.0006783443,0.0004416357,0.0002747275,0.005680534],"category_scores_gemma":[0.00117944,0.0001606184,0.0005618808,0.001651817,0.0002175801,0.0005698663,0.0004556153,0.0003429719,0.0002286823],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002645331,"about_ca_system_score_gemma":0.0007056167,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04937138,"about_ca_topic_score_gemma":0.04723039,"domain_scores_codex":[0.9997318,0.00008843804,0.000008363548,0.00001884208,0.0001217283,0.00003088812],"domain_scores_gemma":[0.9995841,0.000212525,0.00004762828,0.00001885366,0.0001203501,0.0000164429],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002488663,0.0002643409,0.03387081,0.000173711,0.0001428765,0.0003403578,0.00007220814,0.8891858,0.003465019,0.02468734,0.003367633,0.04418105],"study_design_scores_gemma":[0.00003418819,0.0005913962,0.04994071,0.0000275234,0.0001047268,0.00008761421,0.0001816222,0.9325934,0.003526535,0.005908536,0.006977015,0.00002672964],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8942507,0.0003617369,0.02927917,0.0006733823,0.00001976592,0.0002819677,0.003289365,0.0001838197,0.07166026],"genre_scores_gemma":[0.9821212,0.0004167572,0.01095549,0.00002984601,0.00001305065,0.0001242626,0.001395676,0.00001783396,0.004925882],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04937138,"threshold_uncertainty_score":0.09816802,"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."}}