{"id":"W402747145","doi":"","title":"Strategies To Relieve Subway Crowding: Case Study From The Toronto Context","year":2015,"lang":"en","type":"article","venue":"Transportation Research Board 94th Annual MeetingTransportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Downtown; Train; Context (archaeology); Transport engineering; Public transport; Service (business); Business; Transit (satellite); Telecommunications; Computer science; Engineering; Geography; Marketing","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.01639885,0.0005079673,0.0006016605,0.0005708511,0.003712363,0.001062515,0.001330676,0.000407065,0.0005453572],"category_scores_gemma":[0.00132949,0.0004573171,0.0002088766,0.003092235,0.001142559,0.002313501,0.0000189818,0.001637003,0.0002339275],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007426519,"about_ca_system_score_gemma":0.002850615,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7075098,"about_ca_topic_score_gemma":0.8953394,"domain_scores_codex":[0.9835294,0.004308098,0.001516578,0.001454253,0.007116451,0.002075237],"domain_scores_gemma":[0.985629,0.003167108,0.0002553449,0.0008927329,0.008264761,0.001791012],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.001529018,0.0007256729,0.1853775,0.00004117919,0.0001800528,0.002301317,0.7498288,0.007893762,0.00007726331,0.01624744,0.03280169,0.002996254],"study_design_scores_gemma":[0.001737751,0.0008631439,0.1172296,0.0001044686,0.0000618807,0.000001381902,0.807065,0.00007639774,0.00005454048,0.000806293,0.07152417,0.0004754475],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9783353,0.0005041247,0.002267397,0.006172965,0.0005607249,0.004622419,0.0006294472,0.0005057497,0.006401926],"genre_scores_gemma":[0.994186,0.0001676666,0.001643791,0.0002965825,0.0006208931,0.0008654761,0.0004951069,0.0001093562,0.001615113],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1878295,"threshold_uncertainty_score":0.9999745,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1368614290903176,"score_gpt":0.4425722116684648,"score_spread":0.3057107825781471,"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."}}