{"id":"W4379880378","doi":"10.28924/2291-8639-21-2023-51","title":"New Computer Experiment Designs Using Continuum Random Cluster Point Process","year":2023,"lang":"en","type":"article","venue":"International Journal of Analysis and Applications","topic":"Computational Geometry and Mesh Generation","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Markov chain Monte Carlo; Markov chain; Random walk; Markov process; Point (geometry); Computer experiment; Computer science; Process (computing); Point process; Mathematics; Convergence (economics); Algorithm; Mathematical optimization; Monte Carlo method; Metropolis–Hastings algorithm; Theoretical computer science; Machine learning; Simulation; Statistics; Programming language","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05098834,0.001030041,0.001576257,0.00259756,0.0008222504,0.001803842,0.002839932,0.001496378,0.005660249],"category_scores_gemma":[0.1216635,0.0008127665,0.001319128,0.002045579,0.002858887,0.002531728,0.002343316,0.002550414,0.0007045181],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001379,"about_ca_system_score_gemma":0.002886611,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005858131,"about_ca_topic_score_gemma":0.0007401236,"domain_scores_codex":[0.9288391,0.05842137,0.001623759,0.003817557,0.006839097,0.0004590031],"domain_scores_gemma":[0.840485,0.1289301,0.006315494,0.01497515,0.008223441,0.001070772],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001978554,0.001140584,0.007115291,0.00199387,0.0009425043,0.000200897,0.001230528,0.1675002,0.008674867,0.4715755,0.002573661,0.3350736],"study_design_scores_gemma":[0.001592455,0.004732809,0.004098541,0.0003338488,0.0004770525,0.0001887619,0.000283721,0.5888929,0.0113391,0.3684064,0.01945465,0.0001997012],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006859206,0.00006685357,0.9903809,0.00007111939,0.00004473891,0.001313533,0.00006393915,0.0002418083,0.0009577008],"genre_scores_gemma":[0.05615303,0.00007808322,0.9371901,0.0001250975,0.00002863809,0.005944656,0.00009020296,0.00006347388,0.0003268576],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05098834,"threshold_uncertainty_score":0.2696553,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02659132094802072,"score_gpt":0.3335825593753807,"score_spread":0.30699123842736,"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."}}