{"id":"W3124076328","doi":"10.2139/ssrn.1089097","title":"Intertechnology Effects in Intelligent Transportation Systems","year":2008,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Ministry of Education, India; Ministry of Earth Sciences; University of Minnesota; California Department of Transportation","keywords":"Intelligent transportation system; Transport engineering; Computer science; Business; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001517029,0.0005107789,0.0008405625,0.001563712,0.00123362,0.002793243,0.000701772,0.001494053,0.01695954],"category_scores_gemma":[0.008406878,0.0005236816,0.001127974,0.001523734,0.001918862,0.00363336,0.001517865,0.001395471,0.0006671199],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001918688,"about_ca_system_score_gemma":0.0009289294,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007431442,"about_ca_topic_score_gemma":0.007791447,"domain_scores_codex":[0.999137,0.00036526,0.00003132918,0.00009152013,0.0001670667,0.0002078527],"domain_scores_gemma":[0.9913738,0.006444566,0.000706855,0.000390552,0.0007299638,0.0003542149],"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":[0.0003031896,0.0003741513,0.0158225,0.0001703906,0.0002198857,0.0007461245,0.0004294073,0.6705727,0.002542274,0.2771131,0.003437038,0.02826926],"study_design_scores_gemma":[0.0001283338,0.0007069469,0.05844388,0.00009286444,0.0004800804,0.0004317398,0.001816417,0.5667938,0.003190636,0.3574295,0.0103365,0.0001492915],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7588087,0.004054949,0.105165,0.005005718,0.0004591158,0.00009316874,0.0004347453,0.0002131151,0.1257653],"genre_scores_gemma":[0.9889781,0.0007768227,0.001223759,0.000128341,0.00008407991,0.00001347918,0.00003744115,0.0000304533,0.008727571],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01695954,"threshold_uncertainty_score":0.05673534,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009652880346839691,"score_gpt":0.2556302733248799,"score_spread":0.2459773929780402,"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."}}