{"id":"W146776107","doi":"10.1007/s11589-012-0843-5","title":"Observations on the application of artificial neural network to predicting ground motion measures","year":2012,"lang":"en","type":"article","venue":"Earthquake Science","topic":"Seismic Performance and Analysis","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"Artificial neural network; Residual; Ground motion; Parametric statistics; Similarity (geometry); Acceleration; Mean squared error; Computer science; Backpropagation; Regression; Motion (physics); Mathematics; Statistics; Artificial intelligence; Algorithm; Geology; Seismology","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":[0.0006593817,0.0000513531,0.00005463122,0.00005604753,0.0002186823,0.00002715506,0.0001603588,0.00001440385,0.000005777195],"category_scores_gemma":[0.00005749124,0.00003841888,0.00002388201,0.0009841518,0.00005923018,0.0002929899,0.00001482483,0.00006291959,0.00002991417],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001847831,"about_ca_system_score_gemma":0.000008079835,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000187757,"about_ca_topic_score_gemma":0.00001694309,"domain_scores_codex":[0.9992989,0.00001009927,0.000128412,0.00008263449,0.0002626387,0.0002173152],"domain_scores_gemma":[0.999666,0.00003618194,0.0000260417,0.0001798349,0.00004225609,0.00004973159],"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":[0.000001573555,0.00001112425,0.02832032,0.00000304982,0.000004221691,1.786101e-8,0.0005853569,0.8708432,0.01819764,0.002084826,0.0001349251,0.0798138],"study_design_scores_gemma":[0.00001162076,0.000009590836,0.4595723,0.00000672974,0.000005618565,3.434292e-7,0.00009674521,0.5367624,0.002774167,0.0000724172,0.0006416888,0.00004644146],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9716172,0.00002614707,0.02748713,0.000204309,0.0002076092,0.00008699206,0.000001250981,0.00004960268,0.0003197549],"genre_scores_gemma":[0.9992168,0.000002032378,0.0002860928,0.0001635338,0.0003026676,0.00001476112,0.000001387154,0.000003987901,0.00000871263],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4312519,"threshold_uncertainty_score":0.168195,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04139585731268426,"score_gpt":0.242928059838349,"score_spread":0.2015322025256647,"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."}}