{"id":"W4399055283","doi":"10.3390/electronics13112064","title":"A Feature-Reduction Scheme Based on a Two-Sample t-Test to Eliminate Useless Spectrogram Frequency Bands in Acoustic Event Detection Systems","year":2024,"lang":"en","type":"article","venue":"Electronics","topic":"Advanced Chemical Sensor Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"","keywords":"Spectrogram; Reduction (mathematics); Event (particle physics); Scheme (mathematics); Feature (linguistics); Computer science; Sample (material); Acoustics; Pattern recognition (psychology); Speech recognition; Artificial intelligence; Physics; Mathematics","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":[0.003163899,0.0009778707,0.001719287,0.002512721,0.001001534,0.00103002,0.0011669,0.0009010719,0.002559709],"category_scores_gemma":[0.01169573,0.0002847711,0.001761571,0.001798501,0.0006899419,0.00110866,0.0008442461,0.001263843,0.001020673],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005458034,"about_ca_system_score_gemma":0.001497717,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002332153,"about_ca_topic_score_gemma":0.002273194,"domain_scores_codex":[0.9965941,0.0007075136,0.0004630654,0.0007638357,0.001253721,0.0002176763],"domain_scores_gemma":[0.9952715,0.002317282,0.0003811986,0.0003903671,0.001494604,0.0001450256],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0009822688,0.0005751367,0.007898468,0.0003003979,0.0003144647,0.0003322249,0.0002371679,0.02517891,0.05215586,0.002397686,0.003685675,0.9059418],"study_design_scores_gemma":[0.0001705384,0.00198989,0.02306033,0.00004671911,0.0002686756,0.001024139,0.0002505689,0.8845716,0.0780488,0.003336617,0.007062824,0.0001692449],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07074136,0.000320342,0.9254932,0.0001495382,0.0002013607,0.000265916,0.0001598781,0.001718384,0.000949885],"genre_scores_gemma":[0.3997787,0.0001529526,0.5969327,0.0001162911,0.000131544,0.0005967667,0.000870107,0.00017985,0.001241164],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003163899,"threshold_uncertainty_score":0.01673251,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005310588603075999,"score_gpt":0.2345713776250693,"score_spread":0.2292607890219933,"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."}}