{"id":"W1769021826","doi":"","title":"A Recursive Logit Model for Routing Policy Choice","year":2015,"lang":"en","type":"article","venue":"","topic":"Game Theory and Voting Systems","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Logit; Econometrics; Mixed logit; Economics; Logistic regression; Routing (electronic design automation); Machine learning","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":[0.002993057,0.0007910772,0.001692246,0.001829473,0.0006604021,0.003162473,0.003436845,0.003944018,0.1019458],"category_scores_gemma":[0.01029484,0.001003456,0.001484732,0.003073785,0.0006262094,0.00385771,0.0009770952,0.00309072,0.01685145],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00278044,"about_ca_system_score_gemma":0.001532168,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02636891,"about_ca_topic_score_gemma":0.02695436,"domain_scores_codex":[0.9985423,0.0006658671,0.00006793159,0.0002838991,0.000178572,0.0002614172],"domain_scores_gemma":[0.995624,0.003134948,0.0003876919,0.0002881441,0.000410476,0.0001548949],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002927509,0.0002284145,0.005880707,0.0002454015,0.0001301695,0.0003726526,0.0002545641,0.4494078,0.0003782796,0.4588917,0.04939516,0.03452245],"study_design_scores_gemma":[0.0001615691,0.00007788852,0.001662876,0.0000646453,0.00006579206,0.0001334237,0.0001000489,0.8522722,0.0001396883,0.1280368,0.01721064,0.00007429127],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07753975,0.001608556,0.7599522,0.01588429,0.0006257247,0.0007024545,0.07256563,0.003128602,0.06799284],"genre_scores_gemma":[0.6766429,0.002297393,0.07991603,0.0008801841,0.0004215536,0.001344026,0.01996105,0.000495317,0.2180415],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1019458,"threshold_uncertainty_score":0.3410428,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1857773199755309,"score_gpt":0.2996714897580311,"score_spread":0.1138941697825002,"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."}}