{"id":"W2246314474","doi":"","title":"A PROBABILISTIC APPROACH TO DEFINING FREEWAY CAPACITY AND BREAKDOWN","year":2000,"lang":"en","type":"article","venue":"","topic":"Traffic control and management","field":"Engineering","cited_by":72,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Bottleneck; Probabilistic logic; Transport engineering; Probabilistic analysis of algorithms; Statistical model; Process (computing); Highway Capacity Manual; Traffic volume; Computer science; Engineering; Level of service; Machine learning; Artificial intelligence; Operations management","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":[0.005618733,0.001711524,0.001021892,0.004508556,0.001275094,0.003833248,0.005232571,0.002238466,0.003435551],"category_scores_gemma":[0.02438665,0.001482161,0.001628718,0.003695087,0.003702552,0.01243822,0.003034264,0.004085486,0.0005558578],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003960267,"about_ca_system_score_gemma":0.003517901,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01451034,"about_ca_topic_score_gemma":0.01113831,"domain_scores_codex":[0.9919817,0.002458308,0.0004884418,0.0009567641,0.003407448,0.0007074152],"domain_scores_gemma":[0.9846336,0.01008056,0.001809828,0.001182354,0.001915914,0.0003776788],"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":[0.00001470505,0.00003504959,0.001946489,0.0000921849,0.00004405975,0.00008822397,0.0002146372,0.5273232,0.0004867391,0.4548049,0.001372312,0.01357748],"study_design_scores_gemma":[0.000008674619,0.00006329275,0.001673043,0.00007726598,0.00002658656,0.0003778398,0.0001809879,0.617129,0.0007198912,0.3655214,0.01412595,0.00009599067],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004822847,0.0004157769,0.9896376,0.0003962404,0.00004908446,0.00005152718,0.0002351062,0.0001104319,0.004281301],"genre_scores_gemma":[0.6468868,0.001993373,0.343662,0.0004623066,0.0005030409,0.0008124388,0.0009071893,0.0002691215,0.004503661],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01451034,"threshold_uncertainty_score":0.02971506,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00795766266874918,"score_gpt":0.1604653888532503,"score_spread":0.1525077261845011,"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."}}