{"id":"W4412382857","doi":"10.1155/atr/7487314","title":"Travel Time Prediction of Urban Agglomeration Significance Channel: A Case Study on the Cross‐Hangzhou Bay Channel","year":2025,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; Natural Science Foundation of Heilongjiang Province; National Natural Science Foundation of China","keywords":"Bay; Channel (broadcasting); Urban agglomeration; Travel time; Geography; Environmental science; Economies of agglomeration; Meteorology; Transport engineering; Economic geography; Engineering; Telecommunications; Archaeology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000324841,0.0006006483,0.0003396298,0.0008245265,0.0004758332,0.0005585547,0.0007932429,0.0006756754,0.001067875],"category_scores_gemma":[0.00089357,0.00018951,0.0004150652,0.001132626,0.000390294,0.0006628945,0.0004593762,0.0004857497,0.000125891],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001550542,"about_ca_system_score_gemma":0.0010878,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1309491,"about_ca_topic_score_gemma":0.1280872,"domain_scores_codex":[0.9998475,0.00002764981,0.000006347962,0.0000440304,0.00002869138,0.00004578228],"domain_scores_gemma":[0.9995111,0.0002082935,0.000045396,0.00004629921,0.000132233,0.00005658283],"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":[0.0001752011,0.0002399189,0.1190138,0.00003923428,0.00006021195,0.0009335371,0.0001779355,0.8584788,0.001324413,0.0009601168,0.001273343,0.01732351],"study_design_scores_gemma":[0.000005927463,0.00002550531,0.01507965,0.000002764187,0.00001182263,0.00001820106,0.0002002347,0.9838369,0.00047897,0.0001769191,0.0001537843,0.000009371095],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9968255,0.00002210966,0.002394849,0.00005825181,0.000005283998,0.00001051071,0.0002015573,0.00007518198,0.0004068234],"genre_scores_gemma":[0.9982879,0.00002004168,0.001078992,0.000003575206,0.000001774639,0.000007933553,0.0002518845,0.000005428204,0.0003424768],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1309491,"threshold_uncertainty_score":0.2603738,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01019565312849947,"score_gpt":0.2439295613508336,"score_spread":0.2337339082223342,"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."}}