{"id":"W2083226902","doi":"10.1007/s11071-011-0173-5","title":"Fuzzy identification of cutting acoustic emission with extended subtractive cluster analysis","year":2011,"lang":"en","type":"article","venue":"Nonlinear Dynamics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Subtractive color; Identification (biology); Fuzzy logic; Machining; Cluster (spacecraft); Process (computing); Simple (philosophy); Algorithm; Computer science; Square (algebra); Mathematics; Engineering; Artificial intelligence; Mechanical engineering; Physics; Geometry","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":[0.0001526138,0.00009428688,0.0001447535,0.0001142009,0.00008290689,0.00003314282,0.0003842306,0.00004881989,0.000003545531],"category_scores_gemma":[0.00001229506,0.00007574595,0.00006683465,0.000958896,0.000039132,0.0002329889,0.00007945793,0.00008822189,0.000004840838],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002511758,"about_ca_system_score_gemma":0.00003197469,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003233997,"about_ca_topic_score_gemma":0.00007427463,"domain_scores_codex":[0.9991087,0.00002811015,0.0002654209,0.0002817582,0.0001756484,0.0001403033],"domain_scores_gemma":[0.9989732,0.00005358866,0.0002655367,0.0004900005,0.0001625838,0.00005508034],"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":[0.0005122906,0.005350834,0.08605631,0.0003463339,0.002963928,0.0001021323,0.011882,0.2247711,0.1158553,0.1345227,0.0003216891,0.4173154],"study_design_scores_gemma":[0.00009562876,0.00003320527,0.01306983,0.000008721622,0.0001240664,0.000005226958,0.00005394184,0.9840624,0.001686824,0.0007581976,0.0000080674,0.00009393792],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2529306,0.0000050983,0.7459549,0.00009988763,0.00003326636,0.0001111497,0.000008237199,0.00004768033,0.0008092682],"genre_scores_gemma":[0.9017325,0.000007840564,0.09797223,0.00003617061,0.00002584754,0.000008696155,0.00002894434,0.00000748329,0.0001802925],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7592913,"threshold_uncertainty_score":0.308883,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01371297098520478,"score_gpt":0.2497697200556232,"score_spread":0.2360567490704184,"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."}}