{"id":"W4394784782","doi":"10.1049/itr2.12512","title":"Flexible optimal bus‐schedule bridging for metro operation‐interruption","year":2024,"lang":"en","type":"article","venue":"IET Intelligent Transport Systems","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Fuzhou University","keywords":"Bridging (networking); Scheduling (production processes); Schedule; Computer science; Engineering; Transport engineering; Computer network; Operations management","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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":[0.0003431343,0.000635889,0.0006864802,0.000468435,0.0004765661,0.0005548539,0.0007236979,0.000461724,0.002729035],"category_scores_gemma":[0.0007455038,0.0003693464,0.0005687071,0.0004555013,0.000327842,0.0005574437,0.0005564272,0.0005384273,0.0001429015],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009047023,"about_ca_system_score_gemma":0.001158791,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01672914,"about_ca_topic_score_gemma":0.01193361,"domain_scores_codex":[0.9997562,0.00006792096,0.000005921079,0.00005180822,0.00003362957,0.00008454303],"domain_scores_gemma":[0.9997585,0.00007754602,0.00004741586,0.00002252287,0.0000421879,0.00005181932],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004462319,0.0000196112,0.0003443213,0.00001379567,0.00001062643,0.00003806834,0.00001119897,0.9946632,0.0005480347,0.001138242,0.0002117555,0.002956434],"study_design_scores_gemma":[0.000005914418,0.00002720487,0.0001577972,0.000001347125,0.000004098392,0.000004142849,0.00001188408,0.9990145,0.0001082926,0.0005217018,0.0001406155,0.000002408588],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5698904,0.0002771885,0.4158203,0.0003040616,0.00008237297,0.0001082839,0.0002939967,0.0003278843,0.01289555],"genre_scores_gemma":[0.9932755,0.00004159561,0.005327321,0.000007931616,0.000006262749,0.00002400863,0.00007311454,0.00001063121,0.001233624],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01672914,"threshold_uncertainty_score":0.03326356,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0672747397271949,"score_gpt":0.3583199778883722,"score_spread":0.2910452381611773,"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."}}