{"id":"W4307743495","doi":"10.32920/21428685.v1","title":"Discriminant non-stationary signal features’ clustering using hard and fuzzy cluster labeling","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Pattern recognition (psychology); Linear discriminant analysis; Feature (linguistics); Cluster analysis; Artificial intelligence; Discriminant; Mathematics; Feature vector; Fuzzy logic; Fuzzy clustering; Speech recognition; Computer science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009912688,0.0007634603,0.0007970051,0.00251299,0.0009118323,0.001515356,0.001517873,0.0009852472,0.002804296],"category_scores_gemma":[0.00253053,0.0004358559,0.001184437,0.001747192,0.0009947873,0.001516663,0.000787206,0.001029621,0.001291194],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001495424,"about_ca_system_score_gemma":0.001636635,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008315173,"about_ca_topic_score_gemma":0.008162254,"domain_scores_codex":[0.9989848,0.0001229944,0.00007245629,0.0003095886,0.0004240207,0.00008616952],"domain_scores_gemma":[0.9990159,0.000180485,0.00009618093,0.0001596502,0.0005019198,0.0000456711],"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":[0.0003304034,0.0001604657,0.001626051,0.0002877551,0.0000868089,0.000146026,0.0003653408,0.1560474,0.05010596,0.033431,0.005881216,0.7515316],"study_design_scores_gemma":[0.000008857131,0.00003680644,0.001023336,0.0000150322,0.00001606987,0.00005280487,0.00006001583,0.9766035,0.009967536,0.009362338,0.002824865,0.0000288485],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0113523,0.0001157136,0.9858832,0.0001072251,0.0000426131,0.00007917139,0.00006057883,0.0003832405,0.001976121],"genre_scores_gemma":[0.216864,0.0002360896,0.7746167,0.00009302067,0.00006531777,0.000185238,0.0004761741,0.0002022755,0.007261051],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008315173,"threshold_uncertainty_score":0.01653355,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04614759282987226,"score_gpt":0.314871419779072,"score_spread":0.2687238269491997,"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."}}