{"id":"W4290673809","doi":"10.1155/2022/9009364","title":"A Hybrid Framework for Real-Time Dispatching of Airline Unit Load Devices under Demand Variations","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Advanced Manufacturing and Logistics Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministry of Science and Technology, Taiwan","keywords":"Computer science; Process (computing); Operations research; Stability (learning theory); Tree (set theory); Demand forecasting; Real-time computing; Industrial engineering; Engineering","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":[0.001541708,0.00115203,0.001063757,0.0007123658,0.0003671792,0.00143436,0.001898864,0.00101552,0.00277392],"category_scores_gemma":[0.00142089,0.0005290663,0.001227919,0.0008419424,0.0005766329,0.001192892,0.0008303829,0.0009187979,0.0003010761],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009786418,"about_ca_system_score_gemma":0.001427223,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01196572,"about_ca_topic_score_gemma":0.008717901,"domain_scores_codex":[0.999303,0.0002645308,0.00003205596,0.0001185072,0.0001856433,0.00009627198],"domain_scores_gemma":[0.9994493,0.0003206951,0.00006093008,0.00002839561,0.00009548568,0.0000452198],"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":[0.00001062234,0.00001046686,0.00005802539,0.00001629053,0.00001449508,0.00001809056,0.00001181366,0.9904427,0.0002677443,0.005458022,0.0001190145,0.003572687],"study_design_scores_gemma":[0.000002295619,0.000008056393,0.00001459342,0.00000111718,0.000002353061,0.000002503175,0.000002466913,0.9987986,0.00003460578,0.0009921099,0.0001395209,0.00000178381],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007464769,0.0001290917,0.9905056,0.00005427367,0.00002832568,0.0000333143,0.00005485632,0.000133336,0.001596431],"genre_scores_gemma":[0.658785,0.0004210612,0.3358079,0.00008109833,0.00009494765,0.0003630269,0.0002724599,0.0001289223,0.004045581],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01196572,"threshold_uncertainty_score":0.02379215,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009647963204491293,"score_gpt":0.2492909937576105,"score_spread":0.2396430305531192,"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."}}