{"id":"W2594840230","doi":"10.1103/physreve.96.030101","title":"Random matrices and the New York City subway system","year":2017,"lang":"en","type":"article","venue":"Physical review. E","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Science Foundation","keywords":"Train; Poisson distribution; Random matrix; Statistics; Matrix (chemical analysis); Mathematics; Statistical physics; Computer science; Engineering; Geography; Physics; Cartography","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":[0.0003022394,0.0002689925,0.0002463251,0.0008323581,0.0004704357,0.0008660454,0.0003850615,0.0003305394,0.002819343],"category_scores_gemma":[0.001523479,0.0001592168,0.0002192494,0.001212234,0.0006686084,0.0007265626,0.0003796555,0.0004728941,0.0001959923],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002279916,"about_ca_system_score_gemma":0.0009577177,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1342817,"about_ca_topic_score_gemma":0.1033064,"domain_scores_codex":[0.9998823,0.00003637407,0.000002917702,0.00002170893,0.00002281327,0.00003394153],"domain_scores_gemma":[0.9991575,0.0003215896,0.0002675062,0.00003451277,0.0001165914,0.0001021694],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008878115,0.00007070722,0.01632956,0.0001089273,0.0001929385,0.0003239711,0.0005557243,0.5287531,0.001234326,0.4254438,0.01026013,0.016638],"study_design_scores_gemma":[0.00004125024,0.00006162738,0.0321107,0.00004633636,0.00004495137,0.00007176751,0.0007097812,0.772562,0.0002779157,0.182198,0.01181393,0.00006174809],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9502644,0.003423533,0.0268601,0.002768308,0.00007388197,0.00002202857,0.0006409474,0.0000767933,0.01586996],"genre_scores_gemma":[0.9930154,0.001374334,0.002169392,0.00004658827,0.00003193408,0.00002083648,0.000189812,0.0000119152,0.003139673],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1342817,"threshold_uncertainty_score":0.2670002,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03162921395524351,"score_gpt":0.3496728699297352,"score_spread":0.3180436559744917,"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."}}