{"id":"W1516250982","doi":"","title":"Testing Daganzo’s Behavioral Theory for Multi-lane Freeway Traffic","year":2002,"lang":"en","type":"preprint","venue":"eScholarship (California Digital Library)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Federal Highway Administration; U.S. Department of Transportation","keywords":"Merge (version control); Bottleneck; Transport engineering; Three-phase traffic theory; Traffic bottleneck; Computer science; Geography; Traffic congestion reconstruction with Kerner's three-phase theory; Engineering; Traffic optimization; Traffic congestion; Floating car data; Operations management; Information retrieval","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":[0.004838165,0.0009722105,0.0004680719,0.001453512,0.001370308,0.001347367,0.003337537,0.001067685,0.009281904],"category_scores_gemma":[0.01632079,0.0004582681,0.001004549,0.0005327965,0.002831675,0.002901603,0.001847362,0.001175231,0.0008139507],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007547177,"about_ca_system_score_gemma":0.002727259,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04101137,"about_ca_topic_score_gemma":0.0294455,"domain_scores_codex":[0.9975984,0.0006439728,0.00009593258,0.0006806383,0.0007832317,0.0001978965],"domain_scores_gemma":[0.9893365,0.006152266,0.001211069,0.001524797,0.001236053,0.0005391917],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002992731,0.003646577,0.4347993,0.0005050079,0.0004082526,0.0004924365,0.006174639,0.06380355,0.02120253,0.3640563,0.01170768,0.09021094],"study_design_scores_gemma":[0.0009714043,0.003809227,0.401856,0.0002940614,0.0002970356,0.0004534991,0.0122601,0.4035385,0.01181492,0.1423534,0.02204175,0.0003100658],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9007412,0.000115048,0.02675536,0.003470888,0.0001453774,0.0005635218,0.0009980706,0.0001558511,0.0670547],"genre_scores_gemma":[0.9869959,0.00008568448,0.008920527,0.0005707988,0.00002323511,0.0004687156,0.0004597544,0.0000359198,0.002439531],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04101137,"threshold_uncertainty_score":0.08154529,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07705680947229802,"score_gpt":0.2975504878060758,"score_spread":0.2204936783337778,"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."}}