{"id":"W2948828813","doi":"10.3390/pr7060346","title":"Numerical Determination of RVE for Heterogeneous Geomaterials Based on Digital Image Processing Technology","year":2019,"lang":"en","type":"article","venue":"Processes","topic":"Rock Mechanics and Modeling","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Higher Education Discipline Innovation Project; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Representative elementary volume; Cohesion (chemistry); Materials science; Digital image; Sample size determination; Digital image processing; Image processing; Compressive strength; Biological system; Composite material; Microstructure; Image (mathematics); Mathematics; Computer science; Statistics; Artificial intelligence; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003768713,0.0001015165,0.000157139,0.0001081805,0.00002397487,0.00004352921,0.00009668722,0.0000688483,0.000007041008],"category_scores_gemma":[0.00008279158,0.00009569165,0.00002471864,0.0001266604,0.000005937425,0.0001383752,0.00001046302,0.00002989446,0.00000647652],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001633424,"about_ca_system_score_gemma":0.0000300687,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":2.95182e-7,"about_ca_topic_score_gemma":1.800594e-7,"domain_scores_codex":[0.9994531,0.000001613749,0.0001746903,0.0001388303,0.00008171777,0.0001500496],"domain_scores_gemma":[0.9996689,0.0000311084,0.00005001262,0.00009488806,0.0001365651,0.00001852405],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001752384,0.0002285651,0.0001875877,0.02266187,0.00003527226,0.000005899145,0.000255527,0.2747926,0.5029005,0.00006999786,0.00002477975,0.1986621],"study_design_scores_gemma":[0.0001477955,0.00008875249,4.062232e-7,0.00008535418,0.000004906205,0.000002401253,0.000008895254,0.5112253,0.4876803,0.0005381905,0.0001373858,0.00008022678],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4495048,0.0002300253,0.5493286,0.00002385037,0.0001126905,0.000302713,0.0000244012,0.0002346275,0.00023822],"genre_scores_gemma":[0.994282,0.000008479275,0.005560048,0.00001082667,0.00002369426,0.0000613828,0.00001316189,0.00003253074,0.000007843832],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5447772,"threshold_uncertainty_score":0.3902192,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007942697634571323,"score_gpt":0.2284610271667301,"score_spread":0.2205183295321588,"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."}}