{"id":"W4414086637","doi":"10.3389/fbuil.2025.1597715","title":"Development of a site and motion proxy-based site amplification model for shallow bedrock profiles using machine learning","year":2025,"lang":"en","type":"article","venue":"Frontiers in Built Environment","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Nonlinear system; Artificial neural network; Gradient boosting; Random forest; Bedrock; Principal component analysis; Motion (physics)","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":[0.0003124386,0.0001008722,0.0001382301,0.0001742518,0.0001310582,0.00001299282,0.0000694787,0.00005123619,0.00001318293],"category_scores_gemma":[0.00001575586,0.00009755854,0.00002413048,0.00006685295,0.00005210655,0.00007917388,0.00001378338,0.00009071334,0.000001108879],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003715701,"about_ca_system_score_gemma":0.0000350123,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002988878,"about_ca_topic_score_gemma":0.00001706806,"domain_scores_codex":[0.999245,0.00003467852,0.0002255704,0.000233204,0.0001178586,0.0001436731],"domain_scores_gemma":[0.999759,0.00001617964,0.00009196803,0.00009872231,0.00000662118,0.00002748489],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000505732,0.00002982321,0.808539,0.00009521549,0.000008331059,1.568621e-7,0.000443988,0.1193469,0.001890495,0.000002411279,0.0001122032,0.06948098],"study_design_scores_gemma":[0.0002692812,0.00001992076,0.05112271,0.00005839704,0.00001107789,1.713957e-7,0.0000695703,0.9394469,0.006534563,0.0001900473,0.002185304,0.00009207103],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4655337,0.0002946139,0.5337464,0.00006825027,0.00003362322,0.0002683607,0.00001885948,0.00001648016,0.00001969977],"genre_scores_gemma":[0.5798752,0.0000369055,0.4197021,0.00008351954,0.000003168615,0.000008805051,0.0001608754,0.000002617647,0.0001267846],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8201,"threshold_uncertainty_score":0.3978322,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01740054343676137,"score_gpt":0.2190119458549112,"score_spread":0.2016114024181498,"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."}}