{"id":"W2562410910","doi":"10.1155/2017/9216864","title":"Three Extensions of Tong and Richardson’s Algorithm for Finding the Optimal Path in Schedule-Based Railway Networks","year":2017,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Hong Kong; Jessie and George Ho Charitable Foundation","keywords":"Schedule; Computer science; Path (computing); Transport engineering; Operations research; Transit (satellite); Algorithm; Mathematical optimization; Public transport; Engineering; Computer network; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007518327,0.00007387153,0.0001748875,0.00008423637,0.0004903484,0.00004027328,0.0001428514,0.00006886169,0.000004976845],"category_scores_gemma":[0.0001059254,0.00005917261,0.0000751506,0.00009167082,0.0001237366,0.0004823834,7.31148e-7,0.0001364336,4.552627e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000229846,"about_ca_system_score_gemma":0.0001129137,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007339093,"about_ca_topic_score_gemma":0.001058581,"domain_scores_codex":[0.9991074,0.00002619365,0.0004043117,0.00009493827,0.000227275,0.0001398367],"domain_scores_gemma":[0.9986677,0.0001994472,0.0007060472,0.00008852762,0.0002859856,0.00005232253],"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":[0.0001898973,0.00005161805,0.03439558,0.00001736928,0.00001957646,0.000009270883,0.006669444,0.9035926,0.0001678548,0.001646728,0.00001857476,0.05322151],"study_design_scores_gemma":[0.002322079,0.0001418452,0.9464791,0.0002784463,0.00007680393,7.319494e-7,0.003249406,0.04619582,0.0001498851,0.0005639424,0.0004244406,0.0001175024],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4233181,0.0001881598,0.5754125,0.0005822547,0.0002564478,0.0002027799,0.00001754897,0.00000561874,0.00001663752],"genre_scores_gemma":[0.8929457,0.0001595766,0.1067492,0.00002212216,0.00008302082,0.000007105547,0.00001724271,0.00000758173,0.000008427911],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9120835,"threshold_uncertainty_score":0.3771413,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02210901316931929,"score_gpt":0.3091386937718797,"score_spread":0.2870296806025604,"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."}}