{"id":"W2794547108","doi":"10.1117/12.2300812","title":"Soil-pipe interaction modeling for pipe behavior prediction with super learning based methods","year":2018,"lang":"en","type":"article","venue":"","topic":"Geotechnical Engineering and Underground Structures","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Okanagan University College; University of British Columbia; University of Regina","funders":"","keywords":"Expansive clay; Adaptability; Mean squared error; Pipeline transport; Feature selection; Computer science; Pipe network analysis; Geotechnical engineering; Feature (linguistics); Deformation (meteorology); Predictive modelling; Machine learning; Artificial intelligence; Data mining; Environmental science; Soil water; Engineering; Soil science; Geology; Mathematics; Statistics; Environmental engineering","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":[0.0009877693,0.0006873358,0.0008477495,0.0007792962,0.0002981827,0.000525753,0.001115167,0.0007979776,0.001183889],"category_scores_gemma":[0.001949321,0.0004743286,0.0008831036,0.0007767973,0.0004514006,0.0008960757,0.0006613535,0.001015274,0.0003283804],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000531926,"about_ca_system_score_gemma":0.0008375483,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008759777,"about_ca_topic_score_gemma":0.005777646,"domain_scores_codex":[0.9996797,0.0001028702,0.00002253774,0.00008249546,0.00007179836,0.00004070916],"domain_scores_gemma":[0.9988042,0.0007481171,0.0001220732,0.0000800742,0.000209951,0.00003560992],"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":[0.00003567612,0.00003437505,0.001230377,0.00002313614,0.00002759517,0.00003070899,0.0000218796,0.9662226,0.001049352,0.0006536058,0.0001805162,0.03048999],"study_design_scores_gemma":[2.781715e-7,0.000003237702,0.00005119628,4.584419e-7,7.60799e-7,0.000001245394,7.05681e-7,0.9997312,0.00006239979,0.000130826,0.00001701609,6.334752e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04637534,0.0002209008,0.9518511,0.00008495749,0.0000196549,0.00003386477,0.0001034171,0.0005600167,0.0007508238],"genre_scores_gemma":[0.8752702,0.0002608646,0.1223802,0.00008246903,0.00003956062,0.0001664461,0.0003253443,0.00005800174,0.001416991],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008759777,"threshold_uncertainty_score":0.01741755,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01779849730409484,"score_gpt":0.2767155090308861,"score_spread":0.2589170117267913,"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."}}