{"id":"W2793541528","doi":"10.2316/journal.206.2018.2.206-5363","title":"COMPETITIVE PRICING BETWEEN HIGH-SPEED RAILWAY AND CIVIL AVIATION BASED ON THE CELLULAR AUTOMATON","year":2018,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Mobile Agent-Based Network Management","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Cellular automaton; Civil aviation; Aviation; Computer science; Business; Transport engineering; Aerospace engineering; Engineering; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006965444,0.0001017852,0.0001230822,0.0002026016,0.0001189595,0.0002936482,0.0003790701,0.00003314661,0.0000149482],"category_scores_gemma":[0.00005973584,0.00007453832,0.00003639496,0.0001319113,0.00005610866,0.000313883,0.0001043532,0.0001083422,0.000006563533],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001000518,"about_ca_system_score_gemma":0.00004015648,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005904225,"about_ca_topic_score_gemma":0.000003493047,"domain_scores_codex":[0.9987449,0.0001060762,0.0003400726,0.0001425379,0.0005628662,0.0001035466],"domain_scores_gemma":[0.9986876,0.0003043035,0.0004596284,0.0001325936,0.0003664527,0.00004948398],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003950733,0.0001277562,0.004907045,0.00002695317,0.0002649165,0.00004598243,0.0009851126,0.09096199,0.001986707,0.8362529,0.001448955,0.06295221],"study_design_scores_gemma":[0.0005740988,0.0002452391,0.03321422,0.000175848,0.00002341193,0.000006858068,0.00002503621,0.9586015,0.00260506,0.003747384,0.000682354,0.0000989962],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1179853,0.00002347579,0.8725131,0.00819383,0.0006333929,0.0001369658,0.000001903185,0.00003363352,0.0004784142],"genre_scores_gemma":[0.9840661,0.00001315698,0.01493724,0.000577956,0.0003776013,0.000001115905,0.000005099385,0.000006197811,0.00001548073],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8676395,"threshold_uncertainty_score":0.3039584,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01146800319261588,"score_gpt":0.2301031761455692,"score_spread":0.2186351729529533,"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."}}