{"id":"W4236875757","doi":"10.32920/ryerson.14658147","title":"Traffic signal coordination for Wellington Street West, Toronto, Ontario","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Platoon; Transport engineering; Destinations; Traffic congestion; Traffic signal; SIGNAL (programming language); Traffic intensity; Traffic volume; Travel time; Service (business); Computer science; Public transport; Fuel efficiency; Telecommunications; Business; Geography; Real-time computing; Engineering; Tourism; Automotive engineering; Control (management); Archaeology; Marketing","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0002983052,0.0007800894,0.0002844667,0.0005812684,0.00255922,0.001529191,0.0005806023,0.0005689654,0.1598744],"category_scores_gemma":[0.0008313955,0.0003150261,0.0002055181,0.001207841,0.0004209288,0.0004915457,0.0006027153,0.0003049539,0.0240898],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01388011,"about_ca_system_score_gemma":0.01999205,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.888145,"about_ca_topic_score_gemma":0.9591644,"domain_scores_codex":[0.9994166,0.00003772861,0.00001402414,0.000147954,0.0002632087,0.0001205531],"domain_scores_gemma":[0.9992124,0.00006152657,0.00003722438,0.00005792125,0.0004289964,0.0002018658],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004734934,0.0001045572,0.01169301,0.0004306226,0.00003119125,0.0006121742,0.00132637,0.01686887,0.01000232,0.02090643,0.6341958,0.3033551],"study_design_scores_gemma":[0.00006752463,0.0000772921,0.0242514,0.00009162659,0.00002076356,0.00009175225,0.00092294,0.01700479,0.003096707,0.001248619,0.9530841,0.00004246112],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1046064,0.005937819,0.04058762,0.009319971,0.001181335,0.000864051,0.04342117,0.005477027,0.7886047],"genre_scores_gemma":[0.1158188,0.00174687,0.008687332,0.0001314066,0.00005829846,0.00009615615,0.009734594,0.0003880651,0.8633385],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1598744,"threshold_uncertainty_score":0.5348331,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01365713959574515,"score_gpt":0.2233237093653507,"score_spread":0.2096665697696055,"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."}}