{"id":"W4410830083","doi":"10.1016/j.tre.2025.104219","title":"Flight scheduling and pricing with high-speed rail coopetition and delay uncertainty","year":2025,"lang":"en","type":"article","venue":"Transportation Research Part E Logistics and Transportation Review","topic":"Aviation Industry Analysis and Trends","field":"Economics, Econometrics and Finance","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Research Grants Council, University Grants Committee; Social Sciences and Humanities Research Council of Canada; National Natural Science Foundation of China","keywords":"Coopetition; Scheduling (production processes); Business; Industrial organization; Computer science; Transport engineering; Operations research; Engineering; Operations management; Microeconomics; Game theory; Economics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002090223,0.0006189024,0.001192448,0.000619541,0.0004402009,0.002803184,0.00158271,0.001480027,0.003692551],"category_scores_gemma":[0.009657466,0.0005639045,0.001222271,0.001801003,0.001678504,0.003503846,0.0005182356,0.002764744,0.0001849289],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003893552,"about_ca_system_score_gemma":0.001636942,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01106613,"about_ca_topic_score_gemma":0.006277269,"domain_scores_codex":[0.999116,0.0002764109,0.00004119553,0.0001503953,0.000189802,0.0002260639],"domain_scores_gemma":[0.9936554,0.004852499,0.0005924532,0.0001946492,0.0004364097,0.0002686038],"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":[0.000221171,0.0001148642,0.003516427,0.0002693087,0.0002015333,0.0002546796,0.00008584111,0.7155297,0.000667378,0.2376789,0.004993308,0.0364668],"study_design_scores_gemma":[0.00003830193,0.000111489,0.004909952,0.00005622213,0.0001290957,0.0001331152,0.0001501804,0.7274354,0.0002134866,0.261757,0.005008112,0.00005762487],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5568079,0.08629458,0.2925602,0.01416423,0.002434544,0.00008390439,0.0007053867,0.0001747125,0.04677452],"genre_scores_gemma":[0.9792083,0.01274073,0.003130363,0.0001616541,0.001140463,0.00001157158,0.0001038585,0.00002491235,0.003478114],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01106613,"threshold_uncertainty_score":0.02824986,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07992881360649118,"score_gpt":0.3226132529897233,"score_spread":0.2426844393832321,"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."}}