{"id":"W2164211305","doi":"10.3141/1777-03","title":"Development of Microsimulation Activity-Based Model for San Francisco: Destination and Mode Choice Models","year":2001,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":107,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Microsimulation; Multinomial logistic regression; Mode choice; Discrete choice; Nested logit; Mode (computer interface); Computer science; Econometrics; Mixed logit; Choice set; Trip distribution; Estimation; Travel survey; Logit; Operations research; Travel behavior; Logistic regression; Economics; Transport engineering; Mathematics; Engineering; Public transport; 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":[0.001042129,0.0007271542,0.0007069258,0.0007362955,0.0004737906,0.000849658,0.002173828,0.000861164,0.00537829],"category_scores_gemma":[0.001760559,0.0005778472,0.001206321,0.0007314466,0.0003716838,0.0008128816,0.0008357008,0.001287949,0.0008160904],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002114077,"about_ca_system_score_gemma":0.001896809,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0758702,"about_ca_topic_score_gemma":0.05289618,"domain_scores_codex":[0.9996188,0.0001476947,0.00001967757,0.00009491875,0.00006501126,0.00005388307],"domain_scores_gemma":[0.9993206,0.0003380967,0.00008908005,0.00003378857,0.0001742781,0.00004415438],"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":[0.00002235407,0.00003067053,0.002367222,0.00002189642,0.00003014582,0.00004378201,0.00006546042,0.9752766,0.0002345534,0.01625452,0.000753728,0.004898983],"study_design_scores_gemma":[0.000005768561,0.0000111875,0.0004063107,0.000005069063,0.000009473599,0.000008445349,0.00001462169,0.9959434,0.00004975185,0.002392811,0.001146887,0.000006302375],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08940358,0.0003059842,0.8958637,0.0005662107,0.0000746533,0.0002245522,0.001924387,0.0006731066,0.01096387],"genre_scores_gemma":[0.771089,0.0006693378,0.198531,0.000146554,0.0000801016,0.001729018,0.002561532,0.0001711398,0.02502246],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0758702,"threshold_uncertainty_score":0.1508572,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1729527413446766,"score_gpt":0.4396683689170565,"score_spread":0.2667156275723799,"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."}}