{"id":"W2518051279","doi":"10.15760/trec.143","title":"Connecting People to Places: Spatiotemporal Analysis of Transit Supply Using Travel-Time Cubes","year":2016,"lang":"en","type":"report","venue":"","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"The Scarborough Hospital; University of Toronto","funders":"National Institute for Transportation and Communities","keywords":"Transit (satellite); Travel time; Computer science; Transit time; Transport engineering; Geography; Business; Engineering; Public transport","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":[0.0009165381,0.0004061656,0.0004025863,0.003915865,0.0003357343,0.001675126,0.0005509066,0.0003857334,0.001562876],"category_scores_gemma":[0.005002897,0.0002499398,0.0009162845,0.008708696,0.0003655755,0.002099113,0.001097262,0.0005441803,0.0003249931],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009058624,"about_ca_system_score_gemma":0.000627298,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0331248,"about_ca_topic_score_gemma":0.02188486,"domain_scores_codex":[0.9992623,0.0002586528,0.0000927143,0.0001513887,0.0001915797,0.00004346725],"domain_scores_gemma":[0.9978626,0.001114916,0.0003860412,0.0002750991,0.0002835744,0.0000777618],"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":[0.0003420598,0.0001913663,0.331449,0.0004223649,0.0007355307,0.0006866471,0.00369501,0.4598243,0.003307624,0.0282279,0.005737146,0.1653811],"study_design_scores_gemma":[0.00001318526,0.00008338271,0.108635,0.00005782315,0.0000861505,0.0002275517,0.003254487,0.8573669,0.001220204,0.01745338,0.01154178,0.00006007711],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7781234,0.0008025659,0.1853541,0.0009400717,0.00006487998,0.0002130931,0.02722993,0.0009811076,0.006290848],"genre_scores_gemma":[0.9123787,0.0005876596,0.07295994,0.00003034684,0.00003738043,0.0001984733,0.01291222,0.00008267113,0.0008125282],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0331248,"threshold_uncertainty_score":0.06586397,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05254210541109886,"score_gpt":0.3547072392298853,"score_spread":0.3021651338187864,"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."}}