{"id":"W2376806263","doi":"","title":"Integrated Model of Urban Transportation and Land Use","year":2013,"lang":"en","type":"article","venue":"Journal of Inner Mongolia University","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Beijing; TRIPS architecture; Sample (material); Monte Carlo method; Transit (satellite); Transport engineering; Computer science; Econometrics; Operations research; Geography; Statistics; Engineering; Mathematics; Public transport","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001104246,0.00003628762,0.00008797843,0.0001073804,0.0000801381,0.00001583574,0.00005893357,0.00004880876,0.00002384816],"category_scores_gemma":[0.00002105623,0.00003364541,0.00002991076,0.0001450254,0.0000755006,0.0007101857,8.959801e-7,0.00007170299,3.816127e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002343282,"about_ca_system_score_gemma":0.00008012388,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002818888,"about_ca_topic_score_gemma":0.0007829026,"domain_scores_codex":[0.9996069,0.00003715903,0.0001289337,0.00004056813,0.0001287601,0.00005766219],"domain_scores_gemma":[0.9993261,0.00002997889,0.0001799959,0.00002614579,0.0003725495,0.00006525887],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001156623,0.00006768746,0.8975676,0.00001264343,0.00004710399,0.000009680148,0.03583012,0.05655991,0.0005966069,0.005770897,0.002697048,0.0007250769],"study_design_scores_gemma":[0.001298469,0.00009704597,0.9584852,0.00008662995,0.0001203359,0.000001075008,0.01517309,0.01947447,0.0001517013,0.0004437174,0.004504702,0.000163553],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9901111,0.0000100636,0.009303831,0.0002116919,0.00003212705,0.00004067794,0.00001451107,0.00000686179,0.0002691635],"genre_scores_gemma":[0.9946763,0.00009644524,0.004530057,0.00001348037,0.00001144495,2.772056e-8,0.000005889945,0.000002034226,0.0006643413],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06091765,"threshold_uncertainty_score":0.4261333,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01794965431740659,"score_gpt":0.2125217568094963,"score_spread":0.1945721024920897,"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."}}