{"id":"W1562396680","doi":"10.1002/atr.1229","title":"Spectral and time‐frequency analyses of freeway traffic flow","year":2013,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Division of Civil, Mechanical and Manufacturing Innovation; National Natural Science Foundation of China","keywords":"Autocorrelation; Spectral density; Occupancy; Traffic flow (computer networking); Power law; Narrowband; White noise; Physics; Statistical physics; Statistics; Computer science; Mathematics; Telecommunications; Engineering","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":[0.00005373094,0.00007755354,0.0001595684,0.0001554508,0.0000131691,0.000007869276,0.00005342646,0.00003162303,0.00005764512],"category_scores_gemma":[0.000003785429,0.00006981933,0.00006248838,0.0001105819,0.00002063074,0.0004611679,4.515792e-7,0.00008668768,0.00000157021],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001477993,"about_ca_system_score_gemma":0.000005681876,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003104205,"about_ca_topic_score_gemma":0.000007835276,"domain_scores_codex":[0.9993817,0.000006430996,0.0003604908,0.00004963882,0.0001244123,0.00007735338],"domain_scores_gemma":[0.9997146,0.00001313757,0.0001052046,0.00005221927,0.00006905376,0.00004579541],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.00001434515,0.00004482616,0.0001935984,0.000112919,0.0001299557,0.000009179187,0.000693684,0.6937766,0.2246292,0.0001854364,0.001476792,0.07873342],"study_design_scores_gemma":[0.004309235,0.001405642,0.7148342,0.0005759117,0.0007092549,0.00005006176,0.001299482,0.1370621,0.1337491,0.003471554,0.001711513,0.0008218954],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9607623,0.0004564889,0.03785094,0.00004198214,0.0001336737,0.0001145148,0.000006170036,0.000242554,0.0003914076],"genre_scores_gemma":[0.9674649,0.0005151988,0.03195171,0.000007240943,0.00003167151,0.000002739564,0.000006996539,0.00001025772,0.00000926047],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7146406,"threshold_uncertainty_score":0.2847149,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007554671019897072,"score_gpt":0.2319529753284973,"score_spread":0.2243983043086003,"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."}}