{"id":"W2761551893","doi":"10.1155/2017/7318917","title":"A Genetic Algorithm Approach for Expedited Crossing of Emergency Vehicles in Connected and Autonomous Intersection Traffic","year":2017,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic control and management","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Intersection (aeronautics); Emergency vehicle; Genetic algorithm; Sequence (biology); Computer science; Algorithm; Real-time computing; Transport engineering; Engineering; Machine learning","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":[0.00006595434,0.00007426572,0.0001671829,0.0001136678,0.00005381864,0.00001890162,0.00005932739,0.00003238142,0.000001523921],"category_scores_gemma":[0.000009957868,0.00007415502,0.00005422233,0.00003737008,0.00001960475,0.000247162,8.461914e-7,0.00006480585,2.134795e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002459904,"about_ca_system_score_gemma":0.000009693383,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005030164,"about_ca_topic_score_gemma":0.00007043665,"domain_scores_codex":[0.9993669,0.000004836838,0.0004036681,0.00006921398,0.00006874149,0.00008659759],"domain_scores_gemma":[0.999617,0.00001161801,0.0002176693,0.00006195879,0.00006499775,0.00002668892],"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":[0.00007701045,0.00003561677,0.0002555271,0.0001267466,0.00003382352,0.000004214574,0.001982834,0.7085814,0.006364481,0.000009166698,0.000006159999,0.282523],"study_design_scores_gemma":[0.003244105,0.0001861108,0.7873514,0.00007980337,0.00006934877,0.000004745106,0.0010236,0.2072128,0.0004408882,0.00008978843,0.0001739461,0.0001235226],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8398815,0.0003611076,0.1591714,0.00001369874,0.000389834,0.0001531109,0.000005874423,0.00001562899,0.000007868372],"genre_scores_gemma":[0.966401,0.0001299007,0.03337548,0.00000100683,0.00006294184,0.00001026615,0.000005229454,0.00001090005,0.000003252888],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7870958,"threshold_uncertainty_score":0.3023954,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01009320901038228,"score_gpt":0.2389885354912201,"score_spread":0.2288953264808379,"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."}}