{"id":"W2525487943","doi":"10.3233/jcm-160654","title":"A trigonometrically scaled multiple tiling approach for error reduction of models built from fuzzy fragments","year":2016,"lang":"en","type":"article","venue":"Journal of Computational Methods in Sciences and Engineering","topic":"Model Reduction and Neural Networks","field":"Physics and Astronomy","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Range (aeronautics); Reduction (mathematics); Grid; Computer science; Set (abstract data type); Fuzzy logic; Algorithm; Mathematics; Mathematical optimization; Geometry; Artificial intelligence","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":[0.0007048872,0.00006392458,0.00017619,0.0002185349,0.00003581518,0.00001699342,0.00008723151,0.00002067802,0.000004457167],"category_scores_gemma":[0.00004229404,0.00004339829,0.00006607203,0.0002438873,0.00003873239,0.000248561,0.00001532334,0.00006336967,2.372994e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001362419,"about_ca_system_score_gemma":0.00002811877,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008438189,"about_ca_topic_score_gemma":3.111184e-8,"domain_scores_codex":[0.9992622,0.00003427212,0.0003567228,0.0001099,0.0001416016,0.00009530279],"domain_scores_gemma":[0.9991544,0.0005007913,0.0001865616,0.00002783052,0.00008189085,0.00004845793],"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":[0.00002531354,0.00003395723,0.0005157507,0.00000510017,0.00001499001,3.791645e-8,0.00006372745,0.9223884,0.004494791,0.001282204,0.000003846879,0.0711719],"study_design_scores_gemma":[0.0005875318,0.00005510373,0.0009888627,0.00005109123,0.000007304782,0.000002450775,0.00009734177,0.9622395,0.0008105884,0.03509276,0.00001237365,0.00005507541],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3363782,0.00008521558,0.6632755,0.00003326626,0.0001423431,0.00004504123,0.000003588698,0.000001640457,0.00003516069],"genre_scores_gemma":[0.5179024,0.000004179949,0.4819941,0.00000102358,0.00009087235,0.000001988401,4.220259e-7,0.000002111672,0.000002873099],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.1815242,"threshold_uncertainty_score":0.1769731,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08522810305479686,"score_gpt":0.3650740665037118,"score_spread":0.2798459634489149,"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."}}