{"id":"W2519478281","doi":"10.1002/atr.1407","title":"A dual control approach for repeated anticipatory traffic control with estimation of network flow sensitivity","year":2016,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Vlaamse Overheid","keywords":"Sensitivity (control systems); Dual (grammatical number); Computer science; Process (computing); Control (management); Traffic flow (computer networking); Flow network; SIGNAL (programming language); Function (biology); Control theory (sociology); Simulation; Real-time computing; Mathematical optimization; Engineering; Artificial intelligence; 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":[0.001020989,0.0001092915,0.000319089,0.00009277826,0.0001516425,0.00001072044,0.0000446092,0.00008558277,0.000005361513],"category_scores_gemma":[0.0001030151,0.00007829942,0.0001081427,0.0002144554,0.0001175406,0.0005748635,1.234943e-7,0.00007152819,1.554144e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004227121,"about_ca_system_score_gemma":0.0001934091,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009670207,"about_ca_topic_score_gemma":0.0001672195,"domain_scores_codex":[0.9985676,0.0001373159,0.0005827051,0.000133187,0.000382952,0.0001962206],"domain_scores_gemma":[0.9979563,0.000378641,0.0008309804,0.00006566508,0.0006815271,0.00008689843],"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":[0.001959727,0.00007828331,0.003483629,0.00003091778,0.00007402177,0.000007665574,0.003516407,0.9795185,0.0007544337,0.0004029291,0.00002125634,0.01015227],"study_design_scores_gemma":[0.05668668,0.003154616,0.6666641,0.001603406,0.002201398,0.00002678681,0.006657922,0.2588369,0.001454002,0.000739003,0.000881156,0.001094019],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3656987,0.00003332274,0.633595,0.0001681396,0.0001106708,0.0002981044,0.00005784645,0.00002223299,0.00001599928],"genre_scores_gemma":[0.9215998,0.00002971189,0.07811528,0.00002805242,0.0001388506,0.00001081708,0.00004807833,0.00001339834,0.00001601353],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7206816,"threshold_uncertainty_score":0.3192958,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01062986241608495,"score_gpt":0.2608418395759114,"score_spread":0.2502119771598265,"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."}}