{"id":"W2054842377","doi":"10.1002/atr.5670410203","title":"Observing freeway ramp merging phenomena in congested traffic","year":2007,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic control and management","field":"Engineering","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Bottleneck; Traffic bottleneck; Transport engineering; Traffic flow (computer networking); Traffic congestion reconstruction with Kerner's three-phase theory; Metropolitan area; Traffic congestion; Traffic speed; Computer science; Traffic optimization; Range (aeronautics); Floating car data; Queue; Traffic conflict; Engineering; Computer network; Geography","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":[0.0003294343,0.0001811118,0.0002116449,0.0010366,0.0003804243,0.0003022049,0.0003314717,0.0002560809,0.0006380077],"category_scores_gemma":[0.0008428852,0.0001486304,0.0001657893,0.0006544268,0.0002340885,0.0004300001,0.0003598809,0.0002376502,0.000109283],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002585866,"about_ca_system_score_gemma":0.0001941521,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003787303,"about_ca_topic_score_gemma":0.006692728,"domain_scores_codex":[0.9996575,0.00008644855,0.00001750818,0.00005678281,0.000126342,0.00005537352],"domain_scores_gemma":[0.9994487,0.0001156986,0.0001873194,0.00004826992,0.0001340784,0.00006597741],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000675291,0.0003841741,0.8799704,0.00009621295,0.00009906328,0.0009957262,0.00491663,0.006987352,0.07215143,0.0002776452,0.0005080121,0.03293811],"study_design_scores_gemma":[0.000008020586,0.0004769102,0.9803615,0.000008604604,0.00003036626,0.000234985,0.001207647,0.009288936,0.007741278,0.00004588669,0.0005759672,0.00001995314],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9994183,0.000007994433,0.0003337808,0.000001767669,7.505231e-7,0.000005201005,0.00003418104,0.00001121953,0.0001869616],"genre_scores_gemma":[0.9994358,0.000008518632,0.0003752506,0.000001643357,0.000001493793,0.000003884534,0.00009855636,0.000001538371,0.00007319151],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003787303,"threshold_uncertainty_score":0.00753051,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006738892265880528,"score_gpt":0.2108353529784321,"score_spread":0.2040964607125516,"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."}}