{"id":"W4411124104","doi":"10.1016/j.tbs.2025.101073","title":"Transportation access equity analysis in two US cities using Bayesian inference-based logsum compensating variation metrics","year":2025,"lang":"en","type":"article","venue":"Travel Behaviour and Society","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"Metropolitan Council","keywords":"Equity (law); Inference; Variation (astronomy); Bayesian probability; Econometrics; Bayesian inference; Computer science; Economics; Artificial intelligence; 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":[0.004328063,0.0004109763,0.0007299641,0.001689048,0.0004820339,0.001131726,0.001226411,0.0008946114,0.001108992],"category_scores_gemma":[0.01361629,0.0003679963,0.001124786,0.002206716,0.0006094876,0.001188495,0.0009999484,0.001051154,0.00009947026],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002209894,"about_ca_system_score_gemma":0.001582564,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.176341,"about_ca_topic_score_gemma":0.1466041,"domain_scores_codex":[0.99823,0.001035636,0.0000721461,0.0002912792,0.0001844784,0.0001864612],"domain_scores_gemma":[0.9909429,0.006335855,0.0006966175,0.0006051706,0.00118745,0.0002320426],"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":[0.0007346254,0.0005828602,0.3728727,0.00006883013,0.0006061498,0.0001965667,0.0006731973,0.5678788,0.0006990768,0.02028185,0.003016338,0.03238908],"study_design_scores_gemma":[0.00002400474,0.00004759961,0.06773447,0.000007731556,0.00005900473,0.00001578724,0.0002077599,0.9275166,0.0001675454,0.003905532,0.000289236,0.00002464495],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9768406,0.00006388266,0.02160406,0.0002397427,0.000007850223,0.00002169545,0.000536563,0.00008496558,0.0006008024],"genre_scores_gemma":[0.993937,0.00001777132,0.005084551,0.0000139894,0.000004364601,0.00001434876,0.0007069886,0.00001310098,0.0002078621],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.176341,"threshold_uncertainty_score":0.3506291,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07243983377327674,"score_gpt":0.410694166096962,"score_spread":0.3382543323236853,"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."}}