{"id":"W1983123393","doi":"10.3141/2146-06","title":"Evaluating, Comparing, and Improving Metro Networks: Application to Plans for Toronto, Canada","year":2010,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Transport engineering; Plan (archaeology); Public transport; Transit (satellite); Work (physics); Metro station; Light rail transit; Computer science; Street network; Operations research; Business; Geography; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.006725369,0.0001899486,0.0003480826,0.000322523,0.00144571,0.0001948434,0.0007983279,0.0001894985,0.0001012707],"category_scores_gemma":[0.0006030399,0.0001645707,0.0001431973,0.001025145,0.0003738307,0.0005689152,0.000004332322,0.001237285,0.000001343437],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003629526,"about_ca_system_score_gemma":0.001902207,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8163256,"about_ca_topic_score_gemma":0.9977984,"domain_scores_codex":[0.9942341,0.0006453317,0.001026787,0.0004140447,0.002860349,0.0008194069],"domain_scores_gemma":[0.9934638,0.001397221,0.0004270659,0.0003146493,0.003808768,0.0005885002],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002155382,0.0002222107,0.8391469,0.0002687233,0.0001687714,0.00001514432,0.02043783,0.04880673,0.004608571,0.02492896,0.01113587,0.04810497],"study_design_scores_gemma":[0.00138304,0.0004156318,0.924135,0.0001144407,0.00007405393,1.395219e-7,0.007951248,0.007788714,0.0002266418,0.0006828518,0.05696337,0.0002648391],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9583271,0.0001167421,0.03530129,0.003164249,0.000928986,0.00185553,0.00008967917,0.00003326824,0.000183161],"genre_scores_gemma":[0.988011,0.0001606851,0.01065224,0.00009222333,0.0004117241,0.000188303,0.00004945132,0.00003894265,0.0003954174],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1814729,"threshold_uncertainty_score":0.9998543,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08918938802012787,"score_gpt":0.4343718717510298,"score_spread":0.3451824837309019,"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."}}