{"id":"W4409119625","doi":"10.1038/s41598-025-89458-3","title":"An integrative TLBO-driven hybrid grey wolf optimizer for the efficient resolution of multi-dimensional, nonlinear engineering problems","year":2025,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Benchmark (surveying); Computer science; Adaptability; Mathematical optimization; Zoom; Local optimum; Artificial intelligence; Nonlinear system; Optimization problem; Machine learning; Algorithm; Mathematics; Engineering","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":[0.00304487,0.000178753,0.0002504102,0.000437608,0.0004380263,0.000436322,0.000791399,0.00004841666,0.00001664658],"category_scores_gemma":[0.001172654,0.0001227742,0.0001257278,0.001264658,0.0002510592,0.0002605651,0.0003100777,0.0001760335,0.00000357918],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008787381,"about_ca_system_score_gemma":0.0004558619,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002428668,"about_ca_topic_score_gemma":0.000004184259,"domain_scores_codex":[0.9971489,0.00009847143,0.0007104218,0.0009019778,0.0007653856,0.000374796],"domain_scores_gemma":[0.9964367,0.0003275962,0.0003023079,0.001507781,0.001312783,0.0001128006],"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":[0.00000615486,0.0001830441,0.00002499414,0.00003343683,0.00003118357,0.00001400346,0.000267861,0.990865,0.004222962,0.0006492643,0.001885467,0.001816645],"study_design_scores_gemma":[0.0002332441,0.00003914409,0.0000646339,0.00008927763,0.00001392856,0.00002254924,0.00002518089,0.9817474,0.01396702,0.0001691123,0.003512145,0.0001163002],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004595391,0.0002366616,0.9893588,0.0003133262,0.00410092,0.001238868,0.000009751417,0.00009771727,0.00004856912],"genre_scores_gemma":[0.1086568,0.000005232649,0.8885843,0.00003034966,0.00003938831,0.0001879206,0.0000473918,0.00001738226,0.002431217],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1040614,"threshold_uncertainty_score":0.5006588,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02055967211135816,"score_gpt":0.2949264101778298,"score_spread":0.2743667380664717,"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."}}