{"id":"W4401108400","doi":"10.1038/s41598-024-67381-3","title":"Application of hybridized ensemble learning and equilibrium optimization in estimating damping ratios of municipal solid waste","year":2024,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Landfill Environmental Impact Studies","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Högskolan i Gävle","keywords":"Boosting (machine learning); Mean squared error; AdaBoost; Gradient boosting; Random forest; Ensemble forecasting; Municipal solid waste; Ensemble learning; Computer science; Correlation coefficient; Regression analysis; Regression; Coefficient of determination; Machine learning; Support vector machine; Statistics; Mathematics; Engineering; Waste management","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":[0.0009648343,0.00009207243,0.0001703805,0.00008265158,0.00008705762,0.00005496609,0.00005478125,0.00002753816,0.00006914305],"category_scores_gemma":[0.0001449528,0.00008307615,0.00002933468,0.0003473137,0.0003162334,0.0003280964,0.0002476665,0.00007359858,0.000004181119],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007418215,"about_ca_system_score_gemma":0.0000100651,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000157784,"about_ca_topic_score_gemma":0.00001228501,"domain_scores_codex":[0.9986138,0.00003910215,0.0004749851,0.0003978517,0.0003085586,0.0001656427],"domain_scores_gemma":[0.9994642,0.00005402293,0.0002261591,0.0002118584,0.000005975976,0.00003781485],"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":[0.000003355265,0.00001911138,0.02046554,0.00005117895,0.000005196262,0.00001127603,0.001371844,0.564503,0.4116596,0.000001927146,0.00003181908,0.001876128],"study_design_scores_gemma":[0.00008265013,0.00002176519,0.000669383,0.00009289539,0.00001261607,0.00003474841,0.0002140932,0.8607994,0.1375182,0.0003858462,0.00008065398,0.00008770796],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9813609,0.0001557468,0.01645156,0.00002525185,0.0004000515,0.0002583363,7.970359e-7,0.00002549031,0.00132189],"genre_scores_gemma":[0.9908143,0.000006833948,0.00890932,0.000002227497,0.00001025121,0.0000146755,0.00001658951,0.00001082254,0.0002150386],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2962964,"threshold_uncertainty_score":0.3387747,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01042586323448192,"score_gpt":0.2657039552933057,"score_spread":0.2552780920588238,"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."}}