{"id":"W4389512140","doi":"10.1016/j.conbuildmat.2023.134443","title":"Shear modulus prediction of landfill components using novel machine learners hybridized with forensic-based investigation optimization","year":2023,"lang":"en","type":"article","venue":"Construction and Building Materials","topic":"Landfill Environmental Impact Studies","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Shear modulus; Mean squared error; Shear (geology); Environmental science; Geotechnical engineering; Materials science; Composite material; Mathematics; Engineering; Statistics","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":[0.0001813666,0.0001205061,0.0001758238,0.00007147947,0.0001538319,0.00002893834,0.00003741783,0.00004106832,0.0001869226],"category_scores_gemma":[0.00002275411,0.00009727242,0.00001523,0.0001815838,0.0003829267,0.0002056973,0.00006671547,0.0000315229,0.000003855119],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007620572,"about_ca_system_score_gemma":0.000004990249,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004211309,"about_ca_topic_score_gemma":0.000002031366,"domain_scores_codex":[0.9992096,0.0000397355,0.0002160279,0.000203298,0.0001915583,0.0001398033],"domain_scores_gemma":[0.9996693,0.0000171361,0.0001620877,0.00009186167,0.000006741326,0.00005292619],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00006315811,0.000008957309,0.06726842,0.00001879728,0.00001311129,4.1212e-7,0.00005460151,0.1403517,0.7919812,0.000008839433,0.00000893391,0.000221881],"study_design_scores_gemma":[0.001615659,0.00007652269,0.05436383,0.00007876293,0.00005146219,0.00003856406,0.0000667303,0.1191962,0.8242759,0.00006322928,0.00002451189,0.0001486105],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.982985,0.000003089418,0.01629269,0.00006651271,0.0002148817,0.000202621,0.0001109802,0.00007561077,0.00004861932],"genre_scores_gemma":[0.9530456,0.00001750697,0.04674908,0.00002852202,0.00001863238,0.000006245225,0.0001130186,0.00001459878,0.000006796501],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03229474,"threshold_uncertainty_score":0.3966654,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02754738899168267,"score_gpt":0.2223045180654387,"score_spread":0.194757129073756,"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."}}