{"id":"W2539490917","doi":"10.1109/icics.2007.4449841","title":"Construction of discriminative positive time-frequency distributions","year":2007,"lang":"en","type":"article","venue":"","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Discriminative model; Instantaneous phase; Time–frequency analysis; SIGNAL (programming language); Computer science; Feature extraction; Pattern recognition (psychology); Artificial intelligence; Feature (linguistics); Signal processing; Process (computing); Energy (signal processing); Point (geometry); Speech recognition; Mathematics; Computer vision; Statistics; Telecommunications","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":[0.0003314599,0.00006252839,0.00008378622,0.00009537083,0.00005630975,0.00002437963,0.0002289107,0.00004117494,0.00003499556],"category_scores_gemma":[0.0000432227,0.00005559621,0.00003924515,0.0003285479,0.0001149304,0.0004205199,0.00006452938,0.00006665749,0.00002119909],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004047054,"about_ca_system_score_gemma":0.000038866,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003844046,"about_ca_topic_score_gemma":0.000008295545,"domain_scores_codex":[0.999369,0.00004050758,0.0001919108,0.0001451404,0.0001400679,0.0001134072],"domain_scores_gemma":[0.999361,0.00009844604,0.00009032671,0.0002060012,0.0002041461,0.00004008212],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000001302168,0.0000317827,0.0001403153,0.000001018766,0.000005272329,9.583689e-7,0.0004708647,3.041063e-7,0.005839257,0.9850695,0.0001596437,0.008279837],"study_design_scores_gemma":[0.0001597213,0.0001729169,0.01784498,0.00001754877,0.000007441991,0.00003072563,0.0001926256,0.001181148,0.7989054,0.1811574,0.000158764,0.0001713501],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009136615,0.000006214354,0.9153455,0.0005973732,0.00003719137,0.0001039361,0.00001057752,0.0001839576,0.07457864],"genre_scores_gemma":[0.671042,0.000001272142,0.3286775,0.00006457358,0.000007480285,0.000002294604,0.00001055033,0.000001938645,0.0001924185],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8039121,"threshold_uncertainty_score":0.2267147,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008305820444832128,"score_gpt":0.264159051798063,"score_spread":0.2558532313532308,"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."}}