{"id":"W2966427413","doi":"10.1155/2019/9540386","title":"Generating a Spatiotemporal Dynamic Map for Traffic Analysis Using Macroscopic Fundamental Diagram","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; Science and Technology Commission of Shanghai Municipality","keywords":"Computer science; Abstraction; Fidelity; Diagram; Macro; Resource (disambiguation); Data mining; Cluster analysis; Time horizon; Algorithm; Artificial intelligence; Mathematical optimization; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002769877,0.0004984332,0.0003647215,0.001065052,0.0004332439,0.0006607604,0.0005760745,0.0003561353,0.001731049],"category_scores_gemma":[0.001141533,0.0002541205,0.000767631,0.0008438922,0.0002627636,0.0009392881,0.0009056726,0.0004696253,0.0002266536],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005752317,"about_ca_system_score_gemma":0.0010617,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01005085,"about_ca_topic_score_gemma":0.007391911,"domain_scores_codex":[0.9998711,0.00002299525,0.000007463864,0.00002873253,0.00005332675,0.0000163913],"domain_scores_gemma":[0.9997243,0.00009528269,0.00003130688,0.00005188163,0.00007459755,0.00002252839],"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":[0.00003026929,0.00001984699,0.002235669,0.00004530846,0.00001879122,0.00007753446,0.00009236028,0.9547087,0.003994963,0.009564725,0.0007058025,0.02850598],"study_design_scores_gemma":[0.000001801506,0.000004366872,0.000209863,0.000001466152,0.000002414987,0.000008729925,0.00001273671,0.9965731,0.0004614212,0.002094198,0.0006268527,0.000003068487],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03384146,0.00003902923,0.9632763,0.00005124417,0.00001877252,0.0000421953,0.000321683,0.0007234689,0.001685824],"genre_scores_gemma":[0.6647426,0.0001721132,0.3325565,0.00001494479,0.00001387532,0.000168427,0.001057815,0.0001371011,0.001136632],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01005085,"threshold_uncertainty_score":0.01998472,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01266267745290703,"score_gpt":0.3180262435336976,"score_spread":0.3053635660807905,"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."}}