{"id":"W4404927156","doi":"10.1007/s11440-024-02472-z","title":"A machine learning-based drag model for sand particles in transition flow aided by spherical harmonic analysis and resolved CFD-DEM","year":2024,"lang":"en","type":"article","venue":"Acta Geotechnica","topic":"Hydrology and Sediment Transport Processes","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Northern British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Drag; Solid mechanics; Computational fluid dynamics; Mechanics; Flow (mathematics); Mechanical engineering; Computer science; Physics; Engineering; Thermodynamics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003278095,0.0001377627,0.0002075078,0.00005683898,0.0001005756,0.00002857538,0.0001123575,0.0001438939,0.0003222015],"category_scores_gemma":[0.00002175085,0.0001242913,0.00008893826,0.0005084426,0.0001425406,0.000163214,0.0000168833,0.0002372136,0.000007718658],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003970504,"about_ca_system_score_gemma":0.00001684369,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001388766,"about_ca_topic_score_gemma":0.0003522928,"domain_scores_codex":[0.9989157,0.00003913926,0.0002238945,0.0004169016,0.0001384819,0.0002658464],"domain_scores_gemma":[0.9996562,0.0001154326,0.00002526665,0.0001259337,0.000003230597,0.00007394251],"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":[0.0003160881,0.0002287262,0.004641345,0.00006954753,0.0001270475,0.00002019727,0.0005880141,0.9080919,0.07667165,0.000007896388,0.0005684124,0.008669187],"study_design_scores_gemma":[0.0004185406,0.0001058448,0.0008461398,0.00001230741,0.0002305609,9.39957e-7,0.000005393917,0.9887547,0.006829998,0.0005416548,0.002107898,0.0001460186],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6613322,0.0006245259,0.3336,0.003981967,0.000009365506,0.0002286147,0.00003785399,0.0001513485,0.00003411764],"genre_scores_gemma":[0.9967874,0.0001049748,0.002572787,0.0002787923,0.000004907516,0.00008076446,0.00009110301,0.00001450955,0.0000647266],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3354552,"threshold_uncertainty_score":0.5068451,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009372466443894879,"score_gpt":0.2254475179377913,"score_spread":0.2160750514938964,"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."}}