{"id":"W3092196264","doi":"10.1155/2020/8857502","title":"Real-Time Incident Detection and Capacity Estimation Using Loop Detector Data","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Higher Education Commission, Pakistan; National Natural Science Foundation of China","keywords":"Detector; Induction loop; Cell Transmission Model; Occupancy; Computer science; Real-time computing; Simulation; Data mining; Engineering; Transport engineering; Telecommunications; Traffic congestion","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009977688,0.0000769019,0.0001170797,0.00007506491,0.00002978935,0.00001587955,0.00007636499,0.00003545831,0.000004736306],"category_scores_gemma":[0.00001862604,0.00008036295,0.00002208518,0.0001136027,0.0000119495,0.0008566075,0.00000280344,0.0001067089,7.88622e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003977006,"about_ca_system_score_gemma":0.000006847696,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005287459,"about_ca_topic_score_gemma":0.000013208,"domain_scores_codex":[0.9993945,0.00001006115,0.0003024833,0.00008513071,0.000142115,0.00006573107],"domain_scores_gemma":[0.999675,0.00001178385,0.0001282777,0.00007199097,0.00004670144,0.00006623632],"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":[0.00004876558,0.000009609373,0.00009180358,0.0001096769,0.00003728944,0.00000836946,0.0005896341,0.4791223,0.4529599,0.00001173373,0.00007919173,0.06693172],"study_design_scores_gemma":[0.0006300535,0.0001393526,0.03204978,0.0000700597,0.0001266786,0.00001368775,0.0001247675,0.9309021,0.03542676,0.00004989408,0.0003262636,0.0001405476],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5855555,0.00002841794,0.4139509,0.00002218032,0.00008932786,0.00006740402,0.000008903592,0.0002660021,0.00001136534],"genre_scores_gemma":[0.9528401,0.0002867259,0.04677335,0.00001388516,0.00005650486,9.34798e-7,0.00001499106,0.00001294026,5.938683e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4517798,"threshold_uncertainty_score":0.3277106,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02275088524425205,"score_gpt":0.2461526066504822,"score_spread":0.2234017214062302,"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."}}