{"id":"W2064023337","doi":"10.3141/1774-10","title":"Development of Canadian Architecture for Intelligent Transportation Systems","year":2001,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"IBI Group (Canada); Transport Canada","funders":"","keywords":"Architecture; Stakeholder; Reference architecture; Architecture framework; Enterprise architecture framework; Systems architecture; Intelligent transportation system; Database-centric architecture; Computer science; Engineering; Process management; Transport engineering; Engineering management; Software architecture; Economics; Management","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":[0.005342017,0.001394219,0.000484473,0.005190932,0.006240652,0.007205993,0.002869465,0.001506763,0.006678747],"category_scores_gemma":[0.01018264,0.0007613947,0.001392631,0.004450038,0.002249315,0.003072558,0.002721547,0.002279428,0.002450842],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.06045987,"about_ca_system_score_gemma":0.1700601,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9211231,"about_ca_topic_score_gemma":0.9315753,"domain_scores_codex":[0.9925568,0.0009192456,0.000469823,0.0005538217,0.004706576,0.0007936247],"domain_scores_gemma":[0.9876755,0.0002955969,0.0001347087,0.0004254837,0.01091513,0.0005536076],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007195344,0.0000720735,0.002490882,0.0004062963,0.0000454343,0.0005412923,0.003229205,0.04036272,0.006528715,0.5782031,0.1026595,0.2653888],"study_design_scores_gemma":[0.00002217598,0.00004156458,0.001802475,0.0003851916,0.0000535137,0.0001461867,0.001049601,0.03330208,0.003710503,0.01741681,0.9419685,0.000101341],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01685155,0.002822358,0.5340136,0.009877432,0.001145642,0.003397272,0.003435486,0.004423517,0.4240331],"genre_scores_gemma":[0.101143,0.004221587,0.7813522,0.0007873214,0.00008971941,0.001474452,0.005942036,0.0007448627,0.1042448],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.07887691,"threshold_uncertainty_score":0.4386691,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.079262484898789,"score_gpt":0.3313577551872653,"score_spread":0.2520952702884763,"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."}}