{"id":"W4400776009","doi":"10.1007/s00500-024-09823-8","title":"A sophisticated solution to numerical and engineering optimization problems using Chaotic Beluga Whale Optimizer","year":2024,"lang":"en","type":"article","venue":"Soft Computing","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Whale; Beluga Whale; Chaotic; Computer science; Optimization problem; Mathematical optimization; Fishery; Artificial intelligence; Algorithm; Mathematics; Oceanography; Biology; Geology; Arctic","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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":[0.0006252468,0.0002010183,0.0002247781,0.000414458,0.0002237683,0.0007508941,0.0003677088,0.00007439869,0.00002029412],"category_scores_gemma":[0.0004124645,0.0002095114,0.0000421342,0.001421271,0.0000299525,0.0003765495,0.0005522573,0.000241904,0.00003117924],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001436363,"about_ca_system_score_gemma":0.0001105422,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001691353,"about_ca_topic_score_gemma":1.322484e-7,"domain_scores_codex":[0.9980117,0.00007840643,0.0003704232,0.0006491579,0.0004039023,0.0004864052],"domain_scores_gemma":[0.9989718,0.0002730583,0.00004901809,0.0002941404,0.0001721617,0.0002398587],"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":[0.000001368811,0.00001742718,0.00001473599,0.00009335358,0.00001958151,0.00001714163,0.0003843394,0.9879928,0.0003895368,0.002066579,0.00004958181,0.008953588],"study_design_scores_gemma":[0.0001324659,0.00004048699,0.00003333187,0.0001871541,0.00001195019,0.00007189951,0.000005336119,0.9989886,0.00005345233,0.00006138991,0.0001868628,0.0002270179],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000771982,0.0004793779,0.9962952,0.0008550758,0.0005335876,0.0003692409,0.000001597828,0.0006372267,0.00005670297],"genre_scores_gemma":[0.276547,0.000007420911,0.7231843,0.00005725375,0.0001230101,0.000006909633,0.000004866767,0.00002889981,0.00004024481],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.275775,"threshold_uncertainty_score":0.8543627,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02895273528318259,"score_gpt":0.279551159684461,"score_spread":0.2505984244012784,"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."}}