{"id":"W4315927022","doi":"10.1016/j.soildyn.2023.107772","title":"Site amplification prediction model of shallow bedrock sites based on machine learning models","year":2023,"lang":"en","type":"article","venue":"Soil Dynamics and Earthquake Engineering","topic":"Seismic Performance and Analysis","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Bedrock; Artificial neural network; Geology; Seismic hazard; Machine learning; Empirical modelling; Artificial intelligence; Seismology; Algorithm; Computer science; Simulation; Geomorphology","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":[0.0001329917,0.0001519042,0.0001715841,0.0003094308,0.00005740019,0.00002312964,0.00005614837,0.00007396733,0.000003486096],"category_scores_gemma":[0.00001533062,0.0001647466,0.00006320345,0.0003530227,0.000009081659,0.0001446729,0.00001462775,0.0001846516,0.000007397786],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000296932,"about_ca_system_score_gemma":0.000007561902,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001792909,"about_ca_topic_score_gemma":0.00002538741,"domain_scores_codex":[0.9992728,0.000004496605,0.0002066245,0.0001575861,0.0001510605,0.0002073917],"domain_scores_gemma":[0.9997082,0.00002724066,0.00002680116,0.0001446428,0.00002461944,0.00006848605],"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":[0.000003611488,0.000005435054,0.002102828,0.0001061337,0.0000229139,5.405325e-7,0.0001143329,0.9907412,0.002411391,0.0001772996,0.00000529415,0.004309038],"study_design_scores_gemma":[0.000168169,0.000024674,0.004780926,0.00005251212,0.00002179463,5.161593e-7,0.00002076382,0.9945912,0.0001265844,0.00005869303,0.0000119163,0.0001422505],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8086923,0.00005908197,0.190536,0.00002110331,0.00004082562,0.00004123076,0.00006650615,0.0003860228,0.0001569729],"genre_scores_gemma":[0.9985624,0.0003704367,0.0004348599,0.00001167135,0.00002875668,0.00001078392,0.00044206,0.00003849818,0.0001005124],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1901011,"threshold_uncertainty_score":0.6718171,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01162512361779514,"score_gpt":0.1800016804846873,"score_spread":0.1683765568668921,"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."}}