{"id":"W2971428932","doi":"10.33017/reveciperu2007.0004/","title":"LOS DESAFÍOS CONFRONTADOS POR LOS PROYECTOS CONVENCIONALES DE TRANSPORTE Y EL POTENCIAL DE LOS SISTEMAS INTELIGENTES DE TRANSPORTE PARA UNA CIUDAD EN DESARROLLO, LIMA, PERÚ","year":2019,"lang":"es","type":"article","venue":"Revista ECIPeru","topic":"Business, Innovation, and Economy","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministère des Transports","funders":"","keywords":"Computer science; Business; Transport engineering; 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":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.002156567,0.001024393,0.002077019,0.0006850521,0.0003771752,0.000454625,0.001031368,0.0008255866,0.004083736],"category_scores_gemma":[0.0001927075,0.001230187,0.0008992777,0.0007597479,0.0003346293,0.0008629187,0.00005664726,0.0008539165,0.0009467521],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001274535,"about_ca_system_score_gemma":0.00132688,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006886182,"about_ca_topic_score_gemma":0.0002718611,"domain_scores_codex":[0.9936271,0.000145454,0.002846838,0.00151787,0.000215272,0.001647515],"domain_scores_gemma":[0.9964945,0.0002604634,0.00136403,0.001072333,0.0003491666,0.0004594573],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000401967,0.0005922746,0.9521405,0.004517064,0.0005694736,0.000109527,0.002025177,0.0003194089,0.001320343,0.03597575,0.0004199122,0.001608623],"study_design_scores_gemma":[0.003282616,0.0003227279,0.9114212,0.002142868,0.0003843998,0.000215427,0.0007868068,0.006302979,0.001034063,0.002148928,0.06977811,0.002179883],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.969449,0.009530896,0.01271991,0.0005782483,0.0004408427,0.002244085,0.002386149,0.000129205,0.002521644],"genre_scores_gemma":[0.9897823,0.003246897,0.001316093,0.001255113,0.0008736795,0.0002406901,0.0005455836,0.0002202754,0.002519345],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06935821,"threshold_uncertainty_score":0.9998311,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02219732072083326,"score_gpt":0.2493253788089572,"score_spread":0.2271280580881239,"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."}}