{"id":"W2021655490","doi":"10.1002/atr.134","title":"Analyzing transit service reliability using detailed data from automatic vehicular locator systems","year":2010,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Portland State University; Minnesota Department of Transportation; U.S. Department of Transportation","keywords":"Schedule; Automatic vehicle location; Reliability (semiconductor); Transit (satellite); Transport engineering; Computer science; Transit system; Service (business); Reliability engineering; Engineering; Public transport; Telecommunications; Business","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.0008776468,0.0004088292,0.0002562829,0.002161931,0.0001825395,0.0005156457,0.0003136996,0.000448906,0.0006131139],"category_scores_gemma":[0.005654206,0.0002391949,0.0003755388,0.00175382,0.0001881973,0.0004672559,0.0003300259,0.0002911823,0.0002684053],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005622901,"about_ca_system_score_gemma":0.0002954288,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01546093,"about_ca_topic_score_gemma":0.01247364,"domain_scores_codex":[0.9994425,0.0001531217,0.00004944372,0.00009864898,0.0002124214,0.00004387302],"domain_scores_gemma":[0.9937407,0.002860025,0.001225626,0.0006094462,0.001389163,0.0001749658],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003724651,0.0002188803,0.632015,0.00008174444,0.0002082989,0.0002050632,0.0004238114,0.3296999,0.006044159,0.0006061106,0.0007891422,0.02933548],"study_design_scores_gemma":[0.00002130759,0.0005109883,0.4521732,0.00001267121,0.00005769153,0.0001238084,0.0002994004,0.5426559,0.002679949,0.0005299361,0.0008978033,0.00003740879],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9949785,0.00002009404,0.002863616,0.0000183528,0.000002286032,0.00001941721,0.001644311,0.00007370585,0.0003797591],"genre_scores_gemma":[0.9965322,0.00001274746,0.001218641,0.000002123084,0.000002650658,0.00001048905,0.002100511,0.000004980432,0.0001157235],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01546093,"threshold_uncertainty_score":0.03074187,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02477076312958778,"score_gpt":0.3031405070851009,"score_spread":0.2783697439555132,"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."}}