{"id":"W2091904506","doi":"10.1002/atr.108","title":"The methodology of multiple criteria decision making/aiding in public transportation","year":2010,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":82,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"ELECTRE; Multiple-criteria decision analysis; Public transport; Operations research; Computer science; Heuristic; Decision analysis; Management science; Transport engineering; Engineering; Mathematics; Artificial intelligence","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":[0.002185415,0.0001016507,0.0002406127,0.0002676674,0.000274213,0.00003405849,0.0002259749,0.0001385768,0.00004057441],"category_scores_gemma":[0.0007336958,0.00008467126,0.0001169317,0.000528887,0.0001595834,0.0008158554,4.096887e-7,0.0003300402,4.907267e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003099313,"about_ca_system_score_gemma":0.0001874318,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007585475,"about_ca_topic_score_gemma":0.02522597,"domain_scores_codex":[0.9980196,0.0001692043,0.001004605,0.0001296686,0.0004618345,0.0002151249],"domain_scores_gemma":[0.9970564,0.001378282,0.0008301825,0.00009891689,0.0005667291,0.00006951053],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002626729,0.0003360694,0.3812231,0.0000858865,0.00008053875,0.00005852876,0.1226888,0.1494098,0.0627162,0.0712012,0.0001085513,0.2094646],"study_design_scores_gemma":[0.001399913,0.0001021262,0.9725264,0.0001396919,0.000041979,0.000001840127,0.009694039,0.0001721895,0.001056882,0.008809844,0.00591397,0.0001411357],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.929502,0.00009753659,0.0683414,0.0005390218,0.001245528,0.0001596551,0.00001439035,0.00001621264,0.00008428501],"genre_scores_gemma":[0.9054152,0.0002699801,0.09415786,0.0000249424,0.00008266259,0.000004772668,0.00002418633,0.00001138387,0.000009025034],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5913033,"threshold_uncertainty_score":0.9925611,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05343108239410409,"score_gpt":0.3787978285785567,"score_spread":0.3253667461844525,"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."}}