{"id":"W2129486072","doi":"10.13023/ktc.rr.2008.15","title":"Technology Scan for Electronic Toll Collection","year":2008,"lang":"en","type":"article","venue":"UKnowledge (University of Kentucky)","topic":"Taxation and Legal Issues","field":"Business, Management and Accounting","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Toll; Electronic toll collection; Interoperability; Transport engineering; Data collection; Metropolitan area; Enforcement; Toll road; Congestion pricing; Traffic congestion; Business; Engineering; Computer science; Risk analysis (engineering)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007477632,0.00007776403,0.0001432102,0.0004799812,0.0003618842,0.00001027818,0.0001809898,0.00008279484,0.0003087034],"category_scores_gemma":[0.00004931213,0.00009785461,0.00007532881,0.0006936496,0.00009899358,0.0003816141,0.000062569,0.00007575315,0.0001683281],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006412942,"about_ca_system_score_gemma":0.00004816758,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004311055,"about_ca_topic_score_gemma":0.001450308,"domain_scores_codex":[0.9994892,0.000003908895,0.00007535495,0.0001668042,0.00007460706,0.0001901399],"domain_scores_gemma":[0.9994642,0.00001211722,0.0001487915,0.0001242446,0.0002425077,0.000008141944],"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":[0.0006214082,0.0006044111,0.0570505,0.0003847382,0.0001781723,0.00002060226,0.001655416,0.00003010639,0.003770772,0.6932604,0.2158035,0.02662001],"study_design_scores_gemma":[0.0009955097,0.00005272734,0.004342855,0.0000182777,0.00003782632,0.000002572348,0.001164874,0.0012914,0.0002039625,0.001946941,0.9898066,0.0001364347],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.856663,0.0003049773,0.004860111,0.001301681,0.0003714937,0.0004786066,0.000002500251,0.0003290434,0.1356885],"genre_scores_gemma":[0.9801799,0.00003606831,0.000367291,0.00004668449,0.000145141,8.301082e-7,0.00001258978,0.000008735312,0.01920278],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7740031,"threshold_uncertainty_score":0.3990395,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01314169979695967,"score_gpt":0.1862388193947309,"score_spread":0.1730971195977712,"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."}}