{"id":"W7095368946","doi":"","title":"Incorporating spatial dependencies in random parameter discrete choice models” Presented at 84th Annual Transportation Research Board Meeting. Accessed July 10, 2005: http://www.milute.mcgill.ca/Research/Senior/Spatial-Mixed-TRB_rev.pdf","year":2004,"lang":"en","type":"article","venue":"","topic":"Irish and British Studies","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Multinomial logistic regression; Mixed logit; Discrete choice; Nested logit; Multinomial distribution; Logistic regression; Logit","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01340157,0.0009432404,0.0009533187,0.0009000195,0.0006227104,0.001826373,0.001559548,0.001405348,0.008092677],"category_scores_gemma":[0.02214947,0.00167677,0.001784091,0.001124869,0.0008638173,0.003164947,0.001665825,0.002544134,0.001115677],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001959966,"about_ca_system_score_gemma":0.001936933,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01651079,"about_ca_topic_score_gemma":0.03742289,"domain_scores_codex":[0.9917799,0.007269872,0.0001418684,0.0003284503,0.0003294586,0.0001503833],"domain_scores_gemma":[0.9746789,0.02282319,0.0008724803,0.0006048857,0.0007901861,0.0002303278],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002972538,0.0003362907,0.01467927,0.000363702,0.0005761839,0.0004442827,0.001157193,0.4271472,0.0005781399,0.4054326,0.0141712,0.1348167],"study_design_scores_gemma":[0.00006159981,0.0001404274,0.002361687,0.0001100911,0.0001166967,0.00008833025,0.0001297763,0.8356724,0.0003126396,0.1474777,0.01346029,0.00006841626],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04295805,0.001243179,0.9413552,0.004153716,0.0002030724,0.0002109341,0.000668812,0.0002953052,0.008911721],"genre_scores_gemma":[0.4436716,0.001858875,0.5393432,0.0006696623,0.0002045464,0.0008191856,0.001208337,0.0001413055,0.01208338],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01651079,"threshold_uncertainty_score":0.07087511,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05990783637970124,"score_gpt":0.3609495042906238,"score_spread":0.3010416679109225,"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."}}