{"id":"W2110180527","doi":"10.3141/2089-05","title":"Integrating Equity Objectives in a Road Network Design Model","year":2008,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":78,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Equity (law); Transport engineering; Gini coefficient; Maximization; Public economics; Network planning and design; Economics; Computer science; Business; Econometrics; Environmental economics; Engineering; Microeconomics; Inequality; Mathematics; Political science; Telecommunications; Economic inequality","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":"codex-gemma-dda1882f352a","candidate_categories":["sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.01213013,0.0002431125,0.0004803044,0.001027356,0.00206918,0.0001313099,0.001178552,0.0002888411,0.0001489129],"category_scores_gemma":[0.0006498292,0.0002016661,0.0003136533,0.003932202,0.001237866,0.001146717,0.000006467008,0.002534396,0.00001101925],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004977011,"about_ca_system_score_gemma":0.00277486,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.02987478,"about_ca_topic_score_gemma":0.1823013,"domain_scores_codex":[0.9891247,0.003209398,0.001554506,0.000437348,0.004411588,0.001262493],"domain_scores_gemma":[0.9939315,0.001466876,0.0005173278,0.0003206421,0.003355755,0.0004078838],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.001468384,0.0003039466,0.304217,0.00006879018,0.00007258186,0.0001859383,0.06933102,0.602091,0.0003070721,0.01224275,0.004786214,0.004925305],"study_design_scores_gemma":[0.001774677,0.0003726246,0.9591889,0.0006097318,0.0000335065,6.433602e-7,0.01153801,0.01023291,0.0001631814,0.0133518,0.002407887,0.0003261719],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9121089,0.0003267816,0.0810264,0.002746853,0.0005263163,0.0014584,0.00002769668,0.00006572257,0.001712945],"genre_scores_gemma":[0.972227,0.001990514,0.02427057,0.00005645641,0.000255892,0.0000775346,0.00001448589,0.00004433762,0.001063182],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6549718,"threshold_uncertainty_score":0.9997668,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2111766238465696,"score_gpt":0.4418380755809317,"score_spread":0.2306614517343621,"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."}}