{"id":"W2807239671","doi":"10.32920/ryerson.14653806.v1","title":"Time-Frequency Feature Analysis","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Linear discriminant analysis; Pattern recognition (psychology); Artificial intelligence; Computer science; Discriminant; Optimal discriminant analysis; Cluster analysis; Feature (linguistics); Time–frequency analysis; Discriminative model; SIGNAL (programming language); Feature extraction; Feature selection; Scatter matrix; Machine learning; Computer vision; Multivariate statistics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003584354,0.0002272083,0.0004139866,0.000435474,0.00004941947,0.0008206064,0.001642373,0.0004106363,0.0003871485],"category_scores_gemma":[0.00003400749,0.0002104215,0.0004358889,0.001240503,0.00002707737,0.0002642696,0.001857035,0.0006556376,0.00007776164],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005697061,"about_ca_system_score_gemma":0.0002690439,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007994915,"about_ca_topic_score_gemma":0.00006212673,"domain_scores_codex":[0.9982628,0.0001800713,0.0002253956,0.0007830958,0.0003680268,0.0001806105],"domain_scores_gemma":[0.9975312,0.00004362113,0.000153551,0.001938813,0.0002506619,0.00008219205],"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":[0.000006023737,0.0009870259,0.003414358,0.0003198972,0.01213894,0.0005621546,0.01904113,0.02037428,0.005044229,0.6025828,0.2873747,0.0481545],"study_design_scores_gemma":[0.000336198,0.0001092076,0.009238437,0.0001809102,0.001498417,0.00003701717,0.000142692,0.8556254,0.02825611,0.08757754,0.01347314,0.003524961],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001646042,0.0002648253,0.9345933,0.004775977,0.0001253417,0.0001535277,0.000003682804,0.001213936,0.05722335],"genre_scores_gemma":[0.07917082,0.00005386211,0.900917,0.002242061,0.00006074082,0.00004802325,0.0001824852,0.00001436856,0.01731067],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8352511,"threshold_uncertainty_score":0.8580741,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01278559406215926,"score_gpt":0.2685580673983602,"score_spread":0.255772473336201,"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."}}