{"id":"W2076432626","doi":"10.1061/40647(259)20","title":"Use of Digital Images to Enhance Discrete Element Modeling","year":2002,"lang":"en","type":"article","venue":"","topic":"Granular flow and fluidized beds","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Discrete element method; Polygon (computer graphics); Element (criminal law); Cluster (spacecraft); Granular material; SPHERES; Computer science; Particle (ecology); Extended discrete element method; Finite element method; Algorithm; Computational science; Geometry; Computer graphics (images); Mathematics; Physics; Engineering; Structural engineering; Mechanics; Mixed finite element method","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":[0.0004283895,0.0005505266,0.0003341483,0.001147412,0.0002067644,0.001029396,0.0006573002,0.0005881641,0.004456405],"category_scores_gemma":[0.00203168,0.0003765253,0.0003353361,0.0005915153,0.0004158193,0.0007551263,0.0007340407,0.0006694465,0.0008542455],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003993865,"about_ca_system_score_gemma":0.0003852557,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001108451,"about_ca_topic_score_gemma":0.001164249,"domain_scores_codex":[0.9997554,0.00003571209,0.0000205046,0.0000397478,0.0001320368,0.00001653706],"domain_scores_gemma":[0.9993275,0.0003151694,0.0000404096,0.000129589,0.0001615521,0.00002579598],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002930253,0.0001135807,0.001292515,0.0005922402,0.00003561475,0.0004612631,0.0003877499,0.1828137,0.2951884,0.02906456,0.003365977,0.4863913],"study_design_scores_gemma":[0.00003284355,0.00006948394,0.0009380018,0.00004116677,0.00002830519,0.0004601971,0.00007392564,0.8009706,0.1643022,0.004701364,0.02832838,0.00005350951],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02028283,0.000202649,0.9734027,0.0002036891,0.00008253056,0.0000859232,0.0001356376,0.001426965,0.004177096],"genre_scores_gemma":[0.1322086,0.0003107506,0.8650057,0.00007848133,0.00002544312,0.00008804626,0.000149346,0.0002035462,0.00193024],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004456405,"threshold_uncertainty_score":0.01490813,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02073644326897753,"score_gpt":0.2275464350670557,"score_spread":0.2068099917980781,"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."}}