{"id":"W4403302076","doi":"10.1016/j.dt.2024.10.003","title":"Multi-Objective optimization for stable and efficient cargo transportation of partial space elevator","year":2024,"lang":"en","type":"article","venue":"Defence Technology","topic":"Advanced Manufacturing and Logistics Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; Shenzhen Science and Technology Innovation Program; Natural Sciences and Engineering Research Council of Canada; Science, Technology and Innovation Commission of Shenzhen Municipality; National Natural Science Foundation of China","keywords":"Elevator; Space (punctuation); Automotive engineering; Computer science; Transport engineering; Engineering; Mathematical optimization; Aerospace engineering; Mathematics","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":[0.0008399244,0.001077996,0.0008445703,0.0005567736,0.0004176732,0.0009147275,0.0006145434,0.0006671192,0.001393488],"category_scores_gemma":[0.001015521,0.0003663995,0.0005840554,0.0003792049,0.0005366624,0.0006388248,0.0007844294,0.0006358169,0.0001337935],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006820884,"about_ca_system_score_gemma":0.001220027,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007375552,"about_ca_topic_score_gemma":0.00405921,"domain_scores_codex":[0.9996337,0.000123577,0.00001317976,0.00006599119,0.00009230289,0.0000711219],"domain_scores_gemma":[0.9997492,0.0001080856,0.00003502007,0.00001221444,0.00007168534,0.00002377889],"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":[0.00002574385,0.00001668615,0.0002935598,0.00003620935,0.00002019695,0.00003360796,0.00001784,0.9881643,0.001069445,0.003123582,0.0002548906,0.006943985],"study_design_scores_gemma":[0.000004823835,0.00002457106,0.00006815428,0.000001746471,0.000004434844,0.000003126531,0.00000515702,0.999087,0.0001594217,0.0005113527,0.0001282953,0.000001933639],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06659444,0.0004291461,0.9253748,0.0002110034,0.00006658984,0.00006847423,0.00004840999,0.0001797915,0.007027259],"genre_scores_gemma":[0.956971,0.000202317,0.03932228,0.00006617772,0.00001880452,0.0001236326,0.00006332506,0.0000336263,0.003198789],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007375552,"threshold_uncertainty_score":0.01466519,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008090696930424628,"score_gpt":0.2302065499887427,"score_spread":0.2221158530583181,"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."}}