{"id":"W4311806007","doi":"10.1139/cjce-2022-0282","title":"Numerical investigation of the performance of geocell-reinforced granular base in inverted pavement systems using nonlinear finite element modeling","year":2022,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Geotechnical Engineering and Soil Stabilization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Reinforcement; Finite element method; Structural engineering; Nonlinear system; Aggregate (composite); Reliability (semiconductor); Base (topology); Granular material; Rut; Quality (philosophy); Geotechnical engineering; Materials science; Computer science; Engineering; Composite material; Mathematics; Asphalt","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0002775587,0.0003799816,0.0004408533,0.0005019153,0.0003139838,0.0005337646,0.0005847971,0.0009304553,0.001109328],"category_scores_gemma":[0.000749012,0.0002416034,0.0004049149,0.000345203,0.0006644103,0.000310604,0.0003340043,0.0002783675,0.0001367908],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004939613,"about_ca_system_score_gemma":0.0004778556,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009466583,"about_ca_topic_score_gemma":0.008007027,"domain_scores_codex":[0.9998852,0.00002478243,0.000007327544,0.00001981296,0.00003476659,0.00002814679],"domain_scores_gemma":[0.9996015,0.0002192176,0.00006341539,0.0000340461,0.00005605453,0.00002586292],"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":[0.00005332325,0.00004977232,0.002964456,0.00004421272,0.00001156633,0.0001077756,0.00006269929,0.9841632,0.009459957,0.0004861163,0.00006559666,0.002531372],"study_design_scores_gemma":[0.000003124554,0.00002793336,0.000610409,0.00000324604,0.000003264331,0.000009805174,0.0000207593,0.9979938,0.001198174,0.0000559225,0.00006992519,0.000003641763],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9725111,0.000108217,0.02223609,0.00005902043,0.00001094003,0.00002594677,0.0001411903,0.00009638906,0.004811208],"genre_scores_gemma":[0.9943612,0.00004764403,0.004929418,0.000005688943,0.000001205921,0.0000139664,0.00004379114,0.000006565117,0.0005905735],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009466583,"threshold_uncertainty_score":0.01882297,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01182356991743952,"score_gpt":0.1679866910405259,"score_spread":0.1561631211230863,"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."}}