{"id":"W2339579809","doi":"10.1080/01441647.2016.1174748","title":"Forty years of modelling rapid transit’s land value uplift in North America: moving beyond the tip of the iceberg","year":2016,"lang":"en","type":"article","venue":"Transport Reviews","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":115,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Proxy (statistics); Transit (satellite); Iceberg; Land use; Econometrics; Value (mathematics); Work (physics); Geography; Regional science; Operations research; Public economics; Economics; Transport engineering; Public transport; Computer science; Engineering","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":[0.001330581,0.0001421769,0.0005197163,0.00003705404,0.0001139174,0.000005357971,0.0006337281,0.00007012809,0.0001627305],"category_scores_gemma":[0.00001631348,0.00006992733,0.0003602276,0.0006205068,0.0005103732,0.0002010259,0.00000656844,0.0001502688,0.000003597872],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002734198,"about_ca_system_score_gemma":0.0001345437,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004798633,"about_ca_topic_score_gemma":0.03425312,"domain_scores_codex":[0.9981156,0.0001564816,0.0008076211,0.000255484,0.0003782339,0.0002865834],"domain_scores_gemma":[0.9990156,0.0001229731,0.000311889,0.0004509994,0.00004046586,0.00005812],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002521946,0.00007360134,0.9426929,0.0001303372,0.00001558286,0.000001568595,0.009971662,0.0003010392,0.0001037813,0.0001713638,0.00002528618,0.04648769],"study_design_scores_gemma":[0.0004922036,0.00002627966,0.9250991,0.0003763536,0.0001476721,1.465766e-7,0.0002912366,0.00006281365,0.000177159,0.0007172365,0.0723799,0.0002299053],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9891201,0.004705245,0.003189465,0.0004758284,0.0001226122,0.0008355714,0.00004420549,0.00001425144,0.00149277],"genre_scores_gemma":[0.9917336,0.007728057,0.0001861244,0.0001159015,0.00005139183,0.00002198795,0.000004585673,0.00001146544,0.0001469066],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07235461,"threshold_uncertainty_score":0.9833692,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03530565671292442,"score_gpt":0.2740711217290226,"score_spread":0.2387654650160982,"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."}}