{"id":"W2056723090","doi":"10.1016/j.soildyn.2006.12.009","title":"Artificial neural network application to estimate kinematic soil pile interaction response parameters","year":2007,"lang":"en","type":"article","venue":"Soil Dynamics and Earthquake Engineering","topic":"Geotechnical Engineering and Soil Mechanics","field":"Engineering","cited_by":48,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"Pile; Kinematics; Artificial neural network; Bending moment; Kinematic wave; Moment (physics); Soil structure interaction; Head (geology); Geotechnical engineering; Foundation (evidence); Structural engineering; Engineering; Geology; Computer science; Finite element method; Artificial intelligence; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004712096,0.0004407279,0.000339835,0.0006048497,0.0002128421,0.0003568259,0.0002996546,0.0006620151,0.001017109],"category_scores_gemma":[0.001986691,0.0002345805,0.0002020013,0.0005910221,0.0001372625,0.000327133,0.0002015471,0.0003215518,0.0002523125],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002647116,"about_ca_system_score_gemma":0.0002737279,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007872422,"about_ca_topic_score_gemma":0.006845957,"domain_scores_codex":[0.9998873,0.00003424614,0.00001059958,0.00002570427,0.00003091748,0.00001125556],"domain_scores_gemma":[0.9991302,0.0005244744,0.00005604307,0.00003161913,0.0002425122,0.00001519622],"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":[0.000288401,0.0001255755,0.004645339,0.00008801188,0.00008667249,0.0001430982,0.00006339112,0.6952374,0.01805975,0.000552539,0.0009691768,0.2797406],"study_design_scores_gemma":[0.000002836504,0.00001268396,0.001010332,0.000002131766,0.000005859609,0.000009111192,0.000005125964,0.9973843,0.001379839,0.0001022274,0.00008219458,0.000003403493],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3405619,0.0006765356,0.6532608,0.0001555726,0.0001570736,0.00004991623,0.000185071,0.001160677,0.003792405],"genre_scores_gemma":[0.9444174,0.0001518825,0.05221546,0.00003345605,0.00002710382,0.00004843871,0.0001330396,0.0000270955,0.002946054],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007872422,"threshold_uncertainty_score":0.01565319,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005828437460185493,"score_gpt":0.2194473741492662,"score_spread":0.2136189366890807,"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."}}