{"id":"W4409577544","doi":"10.1016/j.cstp.2025.101455","title":"Micro-public transport accessibility mapping to enhance local area planning (LAP) in Ahmedabad","year":2025,"lang":"en","type":"article","venue":"Case Studies on Transport Policy","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Public transport; Transport engineering; Environmental planning; Transportation planning; Urban planning; Business; Computer science; Environmental science; Civil engineering; Engineering","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":[0.0003572425,0.0002195674,0.0001295049,0.0007241658,0.001195087,0.00103258,0.0006059593,0.0004040292,0.003958651],"category_scores_gemma":[0.0009197188,0.00018864,0.0002182292,0.001934059,0.0005399557,0.000495616,0.0008162421,0.0003015694,0.0002612025],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003411111,"about_ca_system_score_gemma":0.004604672,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1718769,"about_ca_topic_score_gemma":0.2862091,"domain_scores_codex":[0.9996386,0.0001232578,0.00001414388,0.00003058183,0.00004200473,0.0001514388],"domain_scores_gemma":[0.999347,0.0002113711,0.00008519569,0.00004769943,0.0001702643,0.00013842],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001604745,0.002154302,0.4623455,0.001827615,0.0002317738,0.01943541,0.03726763,0.07608533,0.01677695,0.02894167,0.01848826,0.3348408],"study_design_scores_gemma":[0.0001863485,0.001509935,0.6687665,0.00031423,0.0002917652,0.002553161,0.1843296,0.07037939,0.007052777,0.005894671,0.05853182,0.0001898096],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9836235,0.0001005615,0.001739418,0.0007547271,0.00001544581,0.0001484098,0.0006578601,0.0001056877,0.01285434],"genre_scores_gemma":[0.9960907,0.00006117138,0.001813479,0.0000269878,0.000002775004,0.00003742014,0.0001354472,0.000004827722,0.001827247],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1718769,"threshold_uncertainty_score":0.3417529,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06595646301694419,"score_gpt":0.4027132400691155,"score_spread":0.3367567770521713,"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."}}