{"id":"W2294228174","doi":"","title":"Montreal : from rubber tyres to steel wheels","year":2016,"lang":"en","type":"article","venue":"Tramways & urban transit","topic":"Transport and Economic Policies","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Light rail transit; Transport engineering; Light rail; Transit (satellite); Engineering; Public transport; Urban transit; Natural rubber; Business; Telecommunications; Civil engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000499318,0.0005455301,0.0001767333,0.0008678322,0.006068852,0.005062268,0.001026614,0.001673779,0.09792867],"category_scores_gemma":[0.001134769,0.000342494,0.000215287,0.001521677,0.003122053,0.002239363,0.001676993,0.001664042,0.00851127],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03002049,"about_ca_system_score_gemma":0.02788004,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9557698,"about_ca_topic_score_gemma":0.9800515,"domain_scores_codex":[0.9991313,0.00007515859,0.00001364996,0.0001120695,0.0003974885,0.0002703418],"domain_scores_gemma":[0.9995752,0.00002833053,0.00002059587,0.00002857558,0.0001793918,0.0001679232],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002249005,0.00001276594,0.0006624223,0.00005123728,0.000003252797,0.0001080217,0.0009265685,0.0001616039,0.000257377,0.09026221,0.8559742,0.05155781],"study_design_scores_gemma":[0.000001544329,0.000002833948,0.0008618484,0.0000282148,8.347285e-7,0.00001189946,0.0003285447,0.0000258247,0.00005908357,0.0007353307,0.9979387,0.000005233183],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.005217749,0.02536766,0.0008160512,0.07523704,0.003223006,0.00003849923,0.001251876,0.0003910019,0.8884571],"genre_scores_gemma":[0.04143932,0.007200186,0.0005645878,0.004068148,0.0003649425,0.0000151984,0.0002912548,0.0001384338,0.945918],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.09792867,"threshold_uncertainty_score":0.3276041,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01392051703672151,"score_gpt":0.1810847387648333,"score_spread":0.1671642217281118,"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."}}