{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0001005188,0.0002776363,0.0003307058,0.0001669078,0.0001171096,0.000157049,0.000349693,0.00009643157,0.001661353],"category_scores_gemma":[0.000006221024,0.0002083156,0.0001642369,0.0001553642,0.00006150085,0.0009190826,0.00002048875,0.00007772715,0.003019094],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003122221,"about_ca_system_score_gemma":0.00001589386,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007237437,"about_ca_topic_score_gemma":0.007415765,"domain_scores_codex":[0.998569,0.000004242056,0.0003384164,0.000408729,0.0001537562,0.0005259156],"domain_scores_gemma":[0.9994564,0.00004103394,0.00006711906,0.0003449667,0.0000360316,0.00005444534],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001296081,0.0007069813,0.3505034,0.0002455373,0.0007110491,0.0001408665,0.005790121,0.0002853198,0.06225677,0.05183108,0.3160763,0.2101565],"study_design_scores_gemma":[0.001429588,0.00001623694,0.6291666,0.00008176635,0.0001281301,5.785007e-7,0.0001026249,0.00004362587,0.0007264942,0.003754679,0.3640352,0.000514535],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9529606,0.000133664,0.001696725,0.005250157,0.0004365495,0.0002632964,0.0001053997,0.0003546222,0.03879897],"genre_scores_gemma":[0.9927198,0.00001135258,0.00009393387,0.002827556,0.00162495,0.00002007732,0.0000289805,0.00005150495,0.002621849],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2786632,"threshold_uncertainty_score":0.9993734,"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."}}