{"id":"W3175048231","doi":"10.32866/001c.25224","title":"A Comprehensive Transit Accessibility and Equity Dashboard","year":2021,"lang":"en","type":"article","venue":"Findings","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Dashboard; Equity (law); Public transport; Business; Transit (satellite); Transport engineering; Baseline (sea); Computer science; Engineering; Data science; Political science","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":[0.003105537,0.0008890913,0.0007983122,0.004009697,0.0009405396,0.002484477,0.0008049418,0.0004648146,0.04246091],"category_scores_gemma":[0.01130032,0.0003671981,0.0005423617,0.005481283,0.0003097449,0.002697164,0.002640707,0.001113385,0.006764537],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001171758,"about_ca_system_score_gemma":0.001874948,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01451502,"about_ca_topic_score_gemma":0.02085035,"domain_scores_codex":[0.9977217,0.0005124556,0.0003071072,0.0003407094,0.0009155298,0.000202391],"domain_scores_gemma":[0.9906854,0.0029337,0.0008493541,0.0008362044,0.003869521,0.0008257793],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0007362855,0.001187074,0.1079867,0.0006827879,0.0001629778,0.0001756053,0.002342961,0.004958067,0.001402627,0.006188437,0.5755826,0.2985938],"study_design_scores_gemma":[0.000266154,0.0006447802,0.3240959,0.0006230485,0.0001064547,0.0001492349,0.006358095,0.01409517,0.003738293,0.006497179,0.6431451,0.0002806301],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"software","genre_scores_codex":[0.2770664,0.0002567697,0.02563632,0.00233443,0.0006592103,0.004112076,0.5408676,0.02278654,0.1262807],"genre_scores_gemma":[0.4322121,0.0005127537,0.07040872,0.0007096829,0.0002285476,0.008251719,0.4364533,0.001785497,0.04943755],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.04246091,"threshold_uncertainty_score":0.1420459,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05960213511412883,"score_gpt":0.3654051286617658,"score_spread":0.305802993547637,"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."}}