{"id":"W4404511583","doi":"10.1155/atr/7450495","title":"Evaluating Transit‐Oriented Development Performance: An Integrated Approach Using Multisource Big Data and Interpretable Machine Learning","year":2024,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Shanghai Jiao Tong University","keywords":"Computer science; Development (topology); Big data; Transit (satellite); Machine learning; Artificial intelligence; Data mining; Data science; Transport engineering; Engineering; Public transport; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001491611,0.0001801779,0.0002366254,0.0002988012,0.0002136814,0.0001860454,0.0005089919,0.00005482954,0.000003934592],"category_scores_gemma":[0.00006318917,0.0001571541,0.00003635514,0.0006437927,0.0000370485,0.003467164,0.00002070381,0.0004705975,0.000001159386],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009481644,"about_ca_system_score_gemma":0.0002456881,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002792998,"about_ca_topic_score_gemma":0.00004905365,"domain_scores_codex":[0.9980539,0.0001166163,0.0007508204,0.0003982599,0.0004395193,0.0002408885],"domain_scores_gemma":[0.9990252,0.00008179733,0.0002496373,0.0002481772,0.0002795498,0.0001155922],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006543632,0.00005047932,0.0004967479,0.0001079663,0.00003597822,0.00002706948,0.0178851,0.4295786,0.01903288,0.00008727617,4.350713e-7,0.532632],"study_design_scores_gemma":[0.0002398987,0.000268729,0.001234931,0.0003848825,0.00004147255,0.00008027304,0.002155216,0.9851993,0.008774729,0.00004222981,0.001399149,0.000179232],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4701302,0.0005099272,0.5289399,0.00001630691,0.0002729584,0.00007964372,0.000002598444,0.00004220855,0.000006285925],"genre_scores_gemma":[0.6579428,0.0000780654,0.341839,0.00001153526,0.00004567745,0.000001814877,0.0000528037,0.0000142028,0.00001413394],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5556206,"threshold_uncertainty_score":0.6408557,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09378594199378922,"score_gpt":0.3435304504647875,"score_spread":0.2497445084709983,"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."}}