{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003092342,0.0006333065,0.0003567385,0.001312775,0.001563399,0.002205835,0.0008594691,0.0006731151,0.006700984],"category_scores_gemma":[0.0005848673,0.0002209038,0.0004150447,0.002757894,0.0004062461,0.001485974,0.001391391,0.0004682553,0.002002724],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001148314,"about_ca_system_score_gemma":0.001137779,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01380456,"about_ca_topic_score_gemma":0.01052233,"domain_scores_codex":[0.9996129,0.00009932667,0.00002807307,0.00009192209,0.00008923451,0.00007855798],"domain_scores_gemma":[0.9998124,0.00002566743,0.00003326181,0.00002800113,0.00007663824,0.0000240906],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00009791314,0.0001671945,0.01575789,0.0005739884,0.00008376968,0.0009678225,0.0008241076,0.2933399,0.006276642,0.08595725,0.03697309,0.5589804],"study_design_scores_gemma":[0.00002809916,0.0001710823,0.01901685,0.0003200626,0.0001086438,0.0006435346,0.0046396,0.4808955,0.005035379,0.07601955,0.4130173,0.0001044638],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.2085415,0.01697352,0.5334898,0.005981027,0.00139471,0.0009311433,0.003617788,0.004196547,0.2248741],"genre_scores_gemma":[0.8373308,0.00980744,0.07660089,0.000395175,0.0003183805,0.0004718939,0.003262309,0.0002321998,0.07158086],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.01380456,"threshold_uncertainty_score":0.02744842,"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."}}