{"id":"W4387541971","doi":"10.1080/23249935.2023.2266508","title":"Assessing transportation network redundancy by integrating route diversity and spare capacity","year":2023,"lang":"en","type":"article","venue":"Transportmetrica A Transport Science","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Spare part; Redundancy (engineering); Computer science; Flow network; Travel time; Measure (data warehouse); Operations research; Transport engineering; Data mining; Engineering; Mathematics; Operations management; Mathematical optimization","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.002727021,0.0003019475,0.000381606,0.0006316617,0.006976619,0.0001840137,0.0005796068,0.0001920856,0.00006915226],"category_scores_gemma":[0.00009123715,0.0003225101,0.0001239123,0.009692764,0.001443747,0.002647599,0.00001036046,0.000358222,0.000008527798],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001472479,"about_ca_system_score_gemma":0.0003699364,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005012109,"about_ca_topic_score_gemma":0.003580343,"domain_scores_codex":[0.9957846,0.00008231588,0.0006197076,0.0008924113,0.00157644,0.001044495],"domain_scores_gemma":[0.9985088,0.0001915407,0.0002411372,0.0002430625,0.0002989582,0.0005164928],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002142801,0.00005397765,0.9460811,0.00004388417,0.00001747577,0.00004066527,0.03895746,0.004896604,0.0002529162,0.006275481,0.0002176671,0.003141354],"study_design_scores_gemma":[0.0004561916,0.00003934375,0.9894965,0.0000983141,0.00009756158,8.899416e-7,0.003989964,0.0005109848,0.0001085866,0.0005237836,0.004222732,0.00045514],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9628822,0.0001724327,0.03146419,0.0005602198,0.0004504486,0.0004534821,0.0001896138,0.0008955102,0.002931951],"genre_scores_gemma":[0.9941828,0.0004381782,0.004427472,0.0001133866,0.00008920823,0.00001898591,0.0004114765,0.00002590231,0.0002925824],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04341542,"threshold_uncertainty_score":0.9999227,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05440404151494527,"score_gpt":0.3053206781107964,"score_spread":0.2509166365958512,"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."}}