{"id":"W1903756953","doi":"10.3141/2350-06","title":"Integrated Intervening Opportunities Model for Public Transit Trip Generation–Distribution","year":2013,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"TRIPS architecture; Public transport; Transport engineering; Socioeconomic status; Trip distribution; Trip generation; Work (physics); Distribution (mathematics); Geography; Transit (satellite); Census; Regional science; Engineering; Mathematics; Population; Sociology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.007105704,0.000274432,0.000455458,0.00092516,0.002236239,0.0005682593,0.001059347,0.0003346632,0.0005345692],"category_scores_gemma":[0.0005809322,0.0002276357,0.0005224323,0.001948587,0.0009563641,0.002051207,0.000003054221,0.001476539,0.00001446823],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004500431,"about_ca_system_score_gemma":0.002161856,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01392371,"about_ca_topic_score_gemma":0.07928532,"domain_scores_codex":[0.9918852,0.001645341,0.001720685,0.0004312863,0.003214636,0.001102797],"domain_scores_gemma":[0.9866588,0.0009441065,0.000580625,0.0003316826,0.01090429,0.0005805321],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.002726923,0.001730732,0.1103564,0.0009040112,0.0009930548,0.00008094499,0.1202033,0.149384,0.00741159,0.2230954,0.2704028,0.1127108],"study_design_scores_gemma":[0.009852734,0.001979321,0.3336047,0.001443658,0.0004267616,0.000001309119,0.09480288,0.2283095,0.001858691,0.02642572,0.2995516,0.001743074],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6844081,0.0001956578,0.2913716,0.02015784,0.0007392126,0.002352722,0.0003987745,0.0000909176,0.0002851372],"genre_scores_gemma":[0.9874153,0.0009072561,0.006788183,0.0001052855,0.0003496165,0.0003786426,0.0005721967,0.00005787555,0.003425654],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3030072,"threshold_uncertainty_score":0.9990627,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3592215201654481,"score_gpt":0.4191521983017274,"score_spread":0.05993067813627928,"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."}}