{"id":"W1715191156","doi":"10.3141/2311-03","title":"Impacts of Transit Priority on Signal Coordination: Case Study of Toronto, Ontario, Canada","year":2012,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre for Social Innovation","funders":"","keywords":"Transit (satellite); SIGNAL (programming language); Transport engineering; Bus priority; Signal timing; Downtown; Computer science; Analytic hierarchy process; Traffic signal; Operations research; Engineering; Simulation; Real-time computing; Public transport; Geography","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":[0.0007440612,0.0007778099,0.0003686381,0.001080523,0.006930724,0.001863403,0.00164905,0.001008984,0.003342904],"category_scores_gemma":[0.00248282,0.0003432576,0.0004751934,0.004128948,0.001810667,0.000606473,0.001076894,0.0006653658,0.0003032075],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.07544802,"about_ca_system_score_gemma":0.06346635,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9958245,"about_ca_topic_score_gemma":0.9984428,"domain_scores_codex":[0.998259,0.0002571136,0.00006133541,0.0001344227,0.0006456259,0.0006426471],"domain_scores_gemma":[0.997376,0.0004356873,0.000192835,0.0001043032,0.00137345,0.0005176916],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001905265,0.001918077,0.5632229,0.001979991,0.0004594359,0.05666526,0.04821841,0.119692,0.02098071,0.02440814,0.03636149,0.1241884],"study_design_scores_gemma":[0.000364182,0.001434081,0.6699033,0.0004064367,0.0004632212,0.002641439,0.1685269,0.07602564,0.005485912,0.001948317,0.07242044,0.0003801222],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9732169,0.0004238762,0.001059857,0.0005981967,0.0000195139,0.0003054314,0.001132963,0.00005525088,0.02318794],"genre_scores_gemma":[0.9896408,0.0005172155,0.001356282,0.0001056995,0.000005906098,0.00005021246,0.0005770786,0.00001793378,0.00772881],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07544802,"threshold_uncertainty_score":0.5474163,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0809491796722078,"score_gpt":0.393234923650928,"score_spread":0.3122857439787202,"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."}}