{"id":"W2804809316","doi":"10.1155/2018/4949565","title":"Decision-Support Framework for Selecting the Optimal Road Toll Collection System","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Multiple-criteria decision analysis; SWOT analysis; Ranking (information retrieval); Toll; Operations research; Selection (genetic algorithm); Decision support system; Computer science; Decision analysis; Evidential reasoning approach; Management science; Risk analysis (engineering); Transport engineering; Data mining; Engineering; Machine learning; Business decision mapping; Mathematics; Business","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":[0.003909387,0.001502175,0.001514238,0.002189334,0.001005293,0.002884194,0.001847662,0.001752554,0.004773163],"category_scores_gemma":[0.004591352,0.000455777,0.001215424,0.001539217,0.0007101227,0.001406482,0.001407804,0.001381142,0.0006154845],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001993801,"about_ca_system_score_gemma":0.003501179,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008525749,"about_ca_topic_score_gemma":0.007412607,"domain_scores_codex":[0.9976889,0.001074764,0.0001661141,0.0002474794,0.0006416336,0.0001810586],"domain_scores_gemma":[0.9984214,0.0008966582,0.0001008127,0.0000434632,0.0004540949,0.00008352678],"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":[0.00008835238,0.0001236701,0.0004963389,0.0002986264,0.00005989823,0.0003960042,0.0002268974,0.8724174,0.00122872,0.07363003,0.001639284,0.04939474],"study_design_scores_gemma":[0.00002875961,0.00004215191,0.00007378015,0.00004526929,0.00002020502,0.00003069393,0.000061218,0.9797932,0.0003804334,0.01778962,0.001721928,0.0000126553],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007049022,0.0001611561,0.9873295,0.0002993317,0.00002481605,0.0002116253,0.0001929647,0.0002029775,0.004528724],"genre_scores_gemma":[0.3980339,0.0003784646,0.5977111,0.0001161378,0.00006859316,0.0009193826,0.0005088039,0.00003174922,0.002231762],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008525749,"threshold_uncertainty_score":0.02067512,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009411637542008968,"score_gpt":0.2669230149179326,"score_spread":0.2575113773759237,"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."}}