{"id":"W1489158350","doi":"10.1002/atr.1305","title":"An improved incremental assignment model for parking variable message sign location problem","year":2015,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Smart Parking Systems Research","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Parking guidance and information; Intersection (aeronautics); Variable (mathematics); Computer science; Sign (mathematics); Process (computing); Transport engineering; MATLAB; Software; Engineering; Mathematics; Operating system","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":[0.00136008,0.001061156,0.001567055,0.00102755,0.0008016133,0.001699929,0.002663587,0.001782017,0.007759012],"category_scores_gemma":[0.00326052,0.0006913192,0.00109188,0.0009606233,0.0008687493,0.001568982,0.001554189,0.001629649,0.0006502433],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001449702,"about_ca_system_score_gemma":0.001756406,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02174046,"about_ca_topic_score_gemma":0.01180816,"domain_scores_codex":[0.9993358,0.0002313416,0.00002789978,0.000143602,0.00009662072,0.0001646562],"domain_scores_gemma":[0.9984385,0.0009185663,0.0001599923,0.00004370398,0.0003191805,0.0001200591],"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":[0.00003737544,0.00003254114,0.0004376752,0.00005079857,0.000009994802,0.00006639025,0.00004634261,0.9873318,0.0001891679,0.005857383,0.0006274967,0.005313084],"study_design_scores_gemma":[0.000004454033,0.000008998561,0.00003917836,0.00000235154,0.000003424163,0.000005291096,0.00000931483,0.9984254,0.00002657836,0.001316703,0.0001558074,0.000002479315],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05769949,0.0005220324,0.9278653,0.0005519179,0.0001084025,0.0001930953,0.0002725444,0.0002948249,0.01249248],"genre_scores_gemma":[0.8758982,0.0005575158,0.1109525,0.0001408505,0.00008425968,0.0004864622,0.0004745564,0.0001021986,0.01130338],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02174046,"threshold_uncertainty_score":0.04322785,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02517742623858979,"score_gpt":0.2811417493043983,"score_spread":0.2559643230658085,"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."}}