{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003735688,0.0005983559,0.0003079468,0.006084644,0.001345512,0.004663272,0.001266274,0.001140754,0.07178755],"category_scores_gemma":[0.01287358,0.000426765,0.0004938881,0.005122337,0.0007574452,0.00616256,0.002647801,0.001386129,0.01369959],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002550445,"about_ca_system_score_gemma":0.003711741,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006512696,"about_ca_topic_score_gemma":0.01001268,"domain_scores_codex":[0.9940122,0.001586665,0.0003180316,0.0003920812,0.003364377,0.0003267297],"domain_scores_gemma":[0.9906641,0.003124364,0.0008921014,0.001088967,0.00402839,0.0002020384],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002167039,0.0001616432,0.01503193,0.0006938199,0.0000255806,0.0002910774,0.0005974124,0.004649652,0.00420608,0.09821223,0.04343152,0.8324824],"study_design_scores_gemma":[0.00006707055,0.0005374643,0.01978639,0.00101956,0.00007031193,0.00118745,0.002329886,0.01688246,0.01593528,0.02038832,0.9216444,0.0001513273],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.04848681,0.003835415,0.1806545,0.00427281,0.0009154778,0.00190112,0.00446739,0.003266471,0.7522],"genre_scores_gemma":[0.4384897,0.007444587,0.3029801,0.001618027,0.0003958884,0.002154836,0.007051049,0.0007821248,0.2390837],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.07178755,"threshold_uncertainty_score":0.2401533,"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."}}