{"id":"W2999306202","doi":"10.1155/2020/7382569","title":"Using Clustering Methods in Multinomial Logit Model for Departure Time Choice","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Iran University of Science and Technology","keywords":"Multinomial logistic regression; Cluster analysis; Computer science; Discretization; Set (abstract data type); Choice set; Data set; Utility maximization; Hierarchical clustering; Econometrics; Data mining; Statistics; Mathematics; Machine learning; 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":[0.0003979403,0.00008046538,0.0001937773,0.00008122397,0.0001073807,0.00001878673,0.00009189981,0.00008463421,0.000009827109],"category_scores_gemma":[0.000183164,0.00008465442,0.00008866107,0.0002151064,0.00002495659,0.0005914143,4.779637e-7,0.0001311468,3.223938e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005330535,"about_ca_system_score_gemma":0.0001376442,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002619671,"about_ca_topic_score_gemma":0.0005313699,"domain_scores_codex":[0.9990188,0.00005960149,0.0004843743,0.0001130363,0.0001774343,0.0001467521],"domain_scores_gemma":[0.9991558,0.0001605729,0.0003633244,0.00002893415,0.0001944853,0.00009693348],"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":[0.0002371792,0.00001594154,0.001667024,0.00002168537,0.000007553946,0.000003118556,0.02299246,0.9656554,0.006496132,0.00006439823,0.000008906898,0.002830166],"study_design_scores_gemma":[0.002228014,0.00008526126,0.01524058,0.00008479759,0.00007437592,6.101107e-7,0.002044238,0.977838,0.0004596084,0.0002776456,0.001487816,0.0001790842],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1522481,0.00005233911,0.8467705,0.0005446731,0.0001338771,0.000181115,0.0000144919,0.00001907258,0.00003579253],"genre_scores_gemma":[0.4540018,0.00002235454,0.5456693,0.0001203164,0.0001341366,0.000002290109,0.00002048926,0.000009764506,0.00001946873],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3017537,"threshold_uncertainty_score":0.3452107,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08242723184148797,"score_gpt":0.4070317753956692,"score_spread":0.3246045435541812,"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."}}