{"id":"W601988799","doi":"","title":"A Stochastic Model for Predicting Shockwaves on Freeways","year":2015,"lang":"en","type":"article","venue":"Transportation Research Board 94th Annual MeetingTransportation Research Board","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Detector; Upstream (networking); Series (stratigraphy); Process (computing); Computer science; Traffic congestion; Stochastic modelling; Stochastic process; Function (biology); Time series; Constant (computer programming); Algorithm; Traffic flow (computer networking); Simulation; Mathematical optimization; Engineering; Mathematics; Statistics; Transport engineering; Machine learning","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.005510099,0.0004698807,0.0004839921,0.00174117,0.0006273952,0.0001940509,0.0007297305,0.0003564882,0.00002662136],"category_scores_gemma":[0.0005272584,0.0005012688,0.0002257418,0.001481841,0.0004205634,0.0007450234,0.00001345177,0.001535574,0.00007011391],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003833467,"about_ca_system_score_gemma":0.0003584998,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005697168,"about_ca_topic_score_gemma":0.003496017,"domain_scores_codex":[0.9923491,0.0003034658,0.00105857,0.0009247702,0.003606459,0.001757634],"domain_scores_gemma":[0.9948221,0.0008423465,0.00009204003,0.0006060362,0.002656606,0.0009808169],"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":[0.001259381,0.0003139102,0.001491005,0.0006665692,0.0001739527,0.00003876787,0.01355119,0.7933783,0.0008657752,0.01737183,0.1649288,0.0059605],"study_design_scores_gemma":[0.003251302,0.001343348,0.009516487,0.0004143388,0.0000624058,4.221672e-7,0.00954654,0.9585692,0.001313295,0.004440078,0.01077124,0.0007712945],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4299821,0.0002444221,0.5463688,0.001197649,0.0005895265,0.005585823,0.001639035,0.007772661,0.006619915],"genre_scores_gemma":[0.9888459,0.0001249451,0.007031923,0.00008131894,0.0002834723,0.002092401,0.0005895349,0.0001856278,0.0007648306],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5588638,"threshold_uncertainty_score":0.9997439,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09997445731380195,"score_gpt":0.3646576390350224,"score_spread":0.2646831817212204,"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."}}