{"id":"W4399573431","doi":"10.23977/acss.2024.080315","title":"Analysis of Network Movement Optimization Model Based on Time Series Forecasting and Multi-Objective Integer Optimization","year":2024,"lang":"en","type":"article","venue":"Advances in Computer Signals and Systems","topic":"Power Systems and Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Series (stratigraphy); Integer (computer science); Computer science; Movement (music); Time series; Integer programming; Mathematical optimization; Operations research; Algorithm; Mathematics; Machine learning","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000206535,0.000136848,0.0003207405,0.0002960484,0.00002865839,0.00007596326,0.00005229456,0.00006392804,0.000001697842],"category_scores_gemma":[0.000005324695,0.0001173413,0.00003404587,0.0004808107,0.00002498881,0.0002778608,0.00002728521,0.00006633779,1.418217e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003263548,"about_ca_system_score_gemma":0.000005361328,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009718723,"about_ca_topic_score_gemma":0.000008476285,"domain_scores_codex":[0.9992282,0.00002778236,0.0003134094,0.0002068169,0.00009312195,0.0001307115],"domain_scores_gemma":[0.9996856,0.0001059214,0.00005093497,0.0001028691,0.00003504697,0.00001960409],"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":[0.000004909725,0.000007015607,0.0004417236,0.0002160893,0.0001125534,0.000002718226,0.0001599045,0.9951764,0.00001018853,0.0002169123,0.00001062511,0.003640909],"study_design_scores_gemma":[0.0001088694,0.00006049238,0.00001744528,0.0005863192,0.00004089141,9.281922e-7,0.0000326732,0.9989595,0.00002343024,0.00003626828,0.0000151933,0.0001180247],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001718796,0.008176744,0.989324,0.000006196788,0.0002815769,0.0002001743,0.00001495542,0.0001539512,0.0001236221],"genre_scores_gemma":[0.9265071,0.0003761707,0.07298661,0.000008780868,0.00003934086,0.00003652269,0.00001326809,0.00001660389,0.0000156225],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9247883,"threshold_uncertainty_score":0.4785041,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01196646016152214,"score_gpt":0.2220899158443617,"score_spread":0.2101234556828396,"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."}}