{"id":"W2579160740","doi":"","title":"Unifying Long and Short Distance Personal Travel in a Statewide Planning Model","year":2016,"lang":"en","type":"article","venue":"Transportation Research Board 95th Annual Meeting","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Travel behavior; Travel time; Microsimulation; Consistency (knowledge bases); Travel survey; Transport engineering; Duration (music); Key (lock); Computer science; Econometrics; Geography; Engineering; Economics; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002336441,0.000145639,0.0001934766,0.0003299445,0.0006495943,0.00008179031,0.0001515183,0.0001243654,0.00002217852],"category_scores_gemma":[0.0002263756,0.0001306687,0.0000393019,0.0005629114,0.0003540657,0.0007665657,0.000003436747,0.0002983426,0.000003120913],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001209467,"about_ca_system_score_gemma":0.0002670975,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00266509,"about_ca_topic_score_gemma":0.01548426,"domain_scores_codex":[0.9971794,0.0002628195,0.0004306932,0.0004368012,0.0009832929,0.0007069158],"domain_scores_gemma":[0.9987523,0.0005058388,0.00006536962,0.00008335111,0.0003411584,0.0002519859],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002782137,0.00004943604,0.8040899,0.00007970032,0.00001446186,0.00009091871,0.1599954,0.02280896,0.0008239779,0.004992563,0.00009763095,0.006678869],"study_design_scores_gemma":[0.001280557,0.00007901107,0.9043567,0.001032023,0.00001701207,3.484804e-7,0.07239342,0.01856951,0.0002823824,0.000959248,0.0005485257,0.0004812531],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9364796,0.0001714082,0.06012829,0.001112344,0.00004485888,0.0003397355,0.0001505575,0.00009223476,0.001481],"genre_scores_gemma":[0.9962254,0.0002479503,0.002578629,0.00003648353,0.00005446779,0.00004325055,0.00005125515,0.00002346092,0.0007390922],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1002668,"threshold_uncertainty_score":0.8640578,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06700512215998321,"score_gpt":0.3785951815068256,"score_spread":0.3115900593468424,"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."}}