{"id":"W2296895905","doi":"","title":"Speed, Travel Time, and Delay for Intersections and Road Segments in Montreal Using Cyclist Smartphone GPS Data","year":2016,"lang":"en","type":"article","venue":"Transportation Research Board 95th Annual MeetingTransportation Research Board","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Intersection (aeronautics); Transport engineering; Global Positioning System; Geometric design; Work (physics); Level of service; Computer science; Morning; Engineering; Telecommunications","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002502526,0.0005456783,0.0001947784,0.001636664,0.0005555681,0.0009492696,0.0006679084,0.000264255,0.004596411],"category_scores_gemma":[0.001396716,0.0002198641,0.0005335712,0.00297063,0.0002416845,0.0004391684,0.0004715757,0.000319491,0.00073364],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008308333,"about_ca_system_score_gemma":0.004136568,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.98214,"about_ca_topic_score_gemma":0.9878432,"domain_scores_codex":[0.9998197,0.0000170614,0.000007116224,0.00006530989,0.00004246077,0.00004836499],"domain_scores_gemma":[0.9996094,0.00006210623,0.00006845772,0.00002616604,0.0001804187,0.00005352738],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002342451,0.0001255765,0.9224333,0.0001243637,0.0002237494,0.0001915274,0.000847867,0.03016871,0.001652031,0.0008610223,0.009026393,0.03411121],"study_design_scores_gemma":[0.00001579295,0.00005994752,0.9507542,0.00002910024,0.00006677293,0.00002453599,0.0009828516,0.04226718,0.000447477,0.0000898026,0.005212222,0.00005006845],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9724761,0.0002460283,0.00118965,0.000100735,0.000009500513,0.00007449585,0.02033111,0.0002471534,0.005325211],"genre_scores_gemma":[0.9776179,0.0002190757,0.001793131,0.0000163642,0.000005530184,0.00005562998,0.01620514,0.00003178764,0.004055564],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01786,"threshold_uncertainty_score":0.06028146,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1141686291747915,"score_gpt":0.4253606814032186,"score_spread":0.3111920522284271,"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."}}