{"id":"W2583110309","doi":"10.1002/atr.1443","title":"Short‐term highway traffic flow prediction based on a hybrid strategy considering temporal–spatial information","year":2016,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":82,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Key Research and Development Program of China","keywords":"Traffic flow (computer networking); Term (time); Computer science; Intelligent transportation system; Flow (mathematics); Data mining; Transport engineering; Engineering; Computer network","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":[0.0001409221,0.000153164,0.0001714222,0.0003168874,0.00004314119,0.0000264054,0.00007697556,0.00005509427,0.00001725587],"category_scores_gemma":[0.000009644516,0.0001217472,0.00009658119,0.00009249342,0.00002031099,0.001302219,7.34552e-7,0.0001380675,0.000003552379],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001056976,"about_ca_system_score_gemma":0.00002562699,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":9.438297e-7,"about_ca_topic_score_gemma":0.00001743561,"domain_scores_codex":[0.9988145,0.00001453149,0.000637214,0.00008178592,0.0003083337,0.0001436207],"domain_scores_gemma":[0.9995274,0.00003220008,0.0001492321,0.0001034464,0.000107305,0.00008044462],"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":[0.00006641932,0.00002219647,0.0002413729,0.00004135929,0.0000214795,0.00001057121,0.00008594495,0.6046358,0.001992267,0.00001254021,0.0005140809,0.3923559],"study_design_scores_gemma":[0.008411119,0.002170556,0.4333473,0.001621616,0.0002596387,0.0000446644,0.0003360419,0.4884252,0.04671988,0.000163107,0.01761751,0.0008834318],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3760633,0.00001505195,0.622008,0.00007285087,0.0006209085,0.0001809841,0.0000633886,0.0008594352,0.0001160378],"genre_scores_gemma":[0.9964253,0.000130669,0.003208277,0.00003201509,0.00009643959,0.00001400237,0.00007161854,0.00001891514,0.000002735698],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.620362,"threshold_uncertainty_score":0.4964705,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00692323287800422,"score_gpt":0.2055563276109217,"score_spread":0.1986330947329175,"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."}}