{"id":"W3206238760","doi":"10.1155/2021/5815280","title":"Short-Term Traffic Prediction considering Spatial-Temporal Characteristics of Freeway Flow","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Computer science; Divergence (linguistics); Data mining; Upstream (networking); Computation; Term (time); Gaussian; Artificial intelligence; Support vector machine; k-nearest neighbors algorithm; Point (geometry); Pattern recognition (psychology); Algorithm; Machine learning; Mathematics","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":[0.00009728583,0.0001105996,0.0002425181,0.00012902,0.00002183672,0.00001114956,0.00005734522,0.00006174727,0.00001735211],"category_scores_gemma":[0.00001087085,0.0001202559,0.000108592,0.0001227636,0.00002024455,0.0003249897,0.000001411752,0.000160564,3.853665e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003674354,"about_ca_system_score_gemma":0.0000247839,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":6.43873e-7,"about_ca_topic_score_gemma":0.00004241327,"domain_scores_codex":[0.9988977,0.0000129327,0.0006867019,0.00008267049,0.0002174256,0.0001026134],"domain_scores_gemma":[0.9994965,0.00001797518,0.0001542446,0.00009513825,0.0001798156,0.00005630529],"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":[0.00007841687,0.0001366622,0.008478394,0.0004471784,0.0002014804,0.0001939722,0.001401611,0.537953,0.06755211,0.00007594658,0.0005253815,0.3829558],"study_design_scores_gemma":[0.001431436,0.0002659693,0.9063323,0.0005905363,0.0002748883,0.00006399707,0.000559089,0.03232899,0.05423987,0.0000616784,0.003553452,0.0002977702],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7247967,0.0001516173,0.2736844,0.00002057453,0.0008553833,0.00007998495,0.00005403118,0.0003050286,0.00005231869],"genre_scores_gemma":[0.9867656,0.0007253769,0.01224612,0.000007505645,0.0001089985,0.000003548418,0.0001167307,0.00002105835,0.00000512577],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8978539,"threshold_uncertainty_score":0.4903894,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00841302592656916,"score_gpt":0.2154008882964955,"score_spread":0.2069878623699263,"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."}}