{"id":"W4319455251","doi":"10.1016/j.cemconres.2023.107113","title":"Discrete-element modeling of shear-induced particle migration during concrete pipe flow: Effect of size distribution and concentration of aggregate on formation of lubrication layer","year":2023,"lang":"en","type":"article","venue":"Cement and Concrete Research","topic":"Granular flow and fluidized beds","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; Compute Canada; Université de Sherbrooke","keywords":"Rheology; Discrete element method; Materials science; Particle (ecology); Lubrication; Mechanics; Aggregate (composite); Particle-size distribution; Composite material; Particle size; Porosity; Geotechnical engineering; Geology; Physics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002667498,0.0003617616,0.0005927998,0.0003384889,0.0004621485,0.0006849448,0.0009006947,0.00128395,0.00174497],"category_scores_gemma":[0.0007053477,0.0004897171,0.0004795678,0.0002519145,0.0006324349,0.0003981674,0.0003429018,0.000566313,0.0002177353],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005348486,"about_ca_system_score_gemma":0.0006389271,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01096862,"about_ca_topic_score_gemma":0.00775899,"domain_scores_codex":[0.9999079,0.00002255009,0.000005876822,0.0000166337,0.00002817406,0.00001893958],"domain_scores_gemma":[0.9996464,0.0002037624,0.00004069627,0.00002272801,0.00005196334,0.00003439845],"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":[0.00005062102,0.0000620997,0.0007021939,0.00002352673,0.00000910305,0.00003929936,0.00003966119,0.991433,0.005118286,0.0006463823,0.00005291894,0.001822889],"study_design_scores_gemma":[0.000002493841,0.000006143177,0.000083529,6.954983e-7,0.00000122071,0.000001931503,0.000004930267,0.9992787,0.0005510275,0.00003415343,0.00003390901,0.000001292949],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7735184,0.0002179135,0.2176491,0.0002572865,0.00009465546,0.00007488914,0.0002677678,0.0003726047,0.007547406],"genre_scores_gemma":[0.9865976,0.00008706907,0.01013425,0.00002312912,0.000007653916,0.00003885642,0.00009523296,0.00003715322,0.002979053],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01096862,"threshold_uncertainty_score":0.02180958,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03181774408270484,"score_gpt":0.2910617983446107,"score_spread":0.2592440542619059,"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."}}