{"id":"W6945287142","doi":"10.25318/3410025401-eng","title":"Average expected useful life of new publicly owned public transit assets, Infrastructure Canada","year":2019,"lang":"en","type":"dataset","venue":"Statistics Canada Dissemination","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Public transport; Transit (satellite); Transit system; Value (mathematics); Government (linguistics)","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":[0.0007015333,0.0009795154,0.001024714,0.004583315,0.0008264233,0.00221254,0.002363529,0.0007375467,0.01461026],"category_scores_gemma":[0.00947604,0.000447231,0.001018903,0.01313116,0.0003565175,0.001067563,0.0008979359,0.001637302,0.005501007],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01379382,"about_ca_system_score_gemma":0.01901518,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9424595,"about_ca_topic_score_gemma":0.9499139,"domain_scores_codex":[0.9991341,0.00004539489,0.00009255189,0.000189637,0.0003350484,0.0002032317],"domain_scores_gemma":[0.9945417,0.0006881102,0.0004855528,0.000349071,0.00355409,0.000381607],"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":[0.0001027884,0.00001948009,0.01826642,0.000428042,0.0001013685,0.0000274,0.00002895999,0.002436779,0.00003186389,0.001112424,0.9728183,0.004626265],"study_design_scores_gemma":[0.0004472963,0.00003983691,0.2391897,0.0009262696,0.0002151884,0.0002266963,0.0005831283,0.00886502,0.0006428126,0.002320106,0.7464147,0.0001291961],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001061823,0.00008998433,0.00003845297,0.00008285343,0.00001137147,0.000005441706,0.997929,0.00005067908,0.0007303548],"genre_scores_gemma":[0.007324855,0.0002095105,0.0001592298,0.00004659819,0.000008711198,0.00002955055,0.9907644,0.00002457173,0.001432608],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.05754048,"threshold_uncertainty_score":0.1157587,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01099130798205808,"score_gpt":0.228979168430773,"score_spread":0.217987860448715,"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."}}