{"id":"W4244652998","doi":"10.32920/ryerson.14646210.v1","title":"North Toronto rapid transit line feasibility study","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Transport engineering; Public transport; Traffic congestion; Transit (satellite); Rush hour; Service (business); Travel time; Business; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001502347,0.0007610397,0.0005723234,0.00111819,0.001464588,0.001533935,0.001390719,0.001086533,0.03610823],"category_scores_gemma":[0.005344559,0.0004872936,0.000625358,0.001499532,0.0006913386,0.001174816,0.0006162552,0.0008464326,0.001951135],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008039779,"about_ca_system_score_gemma":0.008500304,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.4355417,"about_ca_topic_score_gemma":0.4857983,"domain_scores_codex":[0.9980513,0.0009084487,0.00005839819,0.0001625159,0.0004517716,0.0003676129],"domain_scores_gemma":[0.9942338,0.002812901,0.000260672,0.0002138672,0.002075049,0.0004037402],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.003861826,0.00115632,0.02295565,0.001445732,0.0001395991,0.005736405,0.001355612,0.7578096,0.005998515,0.08129382,0.07157825,0.04666875],"study_design_scores_gemma":[0.0008967117,0.002582893,0.03599549,0.0003199658,0.0002001084,0.001025341,0.007903716,0.8488808,0.005029227,0.01244005,0.08455563,0.0001700541],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6362492,0.0005536897,0.02506644,0.002184551,0.0001754093,0.002113625,0.009919336,0.0003104405,0.3234273],"genre_scores_gemma":[0.961064,0.000418044,0.01187313,0.0001574607,0.0000332266,0.0005233188,0.003840207,0.00005860842,0.02203191],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5644584,"threshold_uncertainty_score":0.8660131,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05947842652069528,"score_gpt":0.3462092187070698,"score_spread":0.2867307921863745,"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."}}