{"id":"W4387577538","doi":"10.5593/sgem2023/6.1/s27.52","title":"TRANSPORT MANAGEMENT IN URBAN AREAS","year":2023,"lang":"en","type":"article","venue":"International Multidisciplinary Scientific GeoConference SGEM ...","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Transport Canada","funders":"","keywords":"Traffic congestion; Traffic flow (computer networking); Transport engineering; Computer science; Public transport; Advanced Traffic Management System; Traffic noise; Control (management); Urban area; Intelligent transportation system; Engineering; Computer security","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003630687,0.0001680121,0.0001197837,0.0009328311,0.00009453234,0.0001039132,0.0005472281,0.00005611406,0.0001771462],"category_scores_gemma":[0.000005016326,0.0001783129,0.0000666511,0.0007291196,0.00009274975,0.0003487849,0.0001337559,0.0001435534,0.0005452922],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008592873,"about_ca_system_score_gemma":0.00001239707,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007156833,"about_ca_topic_score_gemma":0.0000590043,"domain_scores_codex":[0.9985256,0.00001199978,0.000312626,0.0003991266,0.0004255651,0.0003251173],"domain_scores_gemma":[0.9995371,0.00001815465,0.00002609985,0.0002944709,0.0000495138,0.00007472768],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001091788,0.0007496846,0.3014216,0.0004035082,0.0005146608,0.00104371,0.006647928,0.1208392,0.002739723,0.0814295,0.2777076,0.2063937],"study_design_scores_gemma":[0.000536283,0.000008983297,0.6615422,0.00008973683,0.00000841224,0.000001910758,0.0005212756,0.289853,0.0001415372,0.001511338,0.0455079,0.0002774932],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8317784,0.00003284017,0.01532198,0.0007226754,0.007520526,0.000955137,0.0001174233,0.009680992,0.13387],"genre_scores_gemma":[0.9932223,0.00004990079,0.0006288794,0.000008410591,0.00004806392,0.0001554362,0.0003406657,0.00001906285,0.005527298],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3601206,"threshold_uncertainty_score":0.7271389,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0182336798054819,"score_gpt":0.2576043236720026,"score_spread":0.2393706438665207,"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."}}