{"id":"W2034874210","doi":"10.1109/isspa.2003.1224879","title":"Time-frequency filtering of interferences in spread spectrum communications","year":2003,"lang":"en","type":"article","venue":"","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Chirp; Chirp spread spectrum; Interference (communication); Direct-sequence spread spectrum; Algorithm; Spread spectrum; SIGNAL (programming language); Time–frequency analysis; Computer science; Signal-to-noise ratio (imaging); Noise (video); Telecommunications; Physics; Artificial intelligence; Optics; Image (mathematics); Radar; Code division multiple access; Channel (broadcasting)","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":[0.0001632611,0.00005487859,0.00009226052,0.0000897762,0.00003249986,0.00004196899,0.0009181926,0.00001926273,0.00009692836],"category_scores_gemma":[0.0000453233,0.00004822889,0.00001932423,0.0003187726,0.00005534872,0.0003107283,0.0001458778,0.00006899156,0.00003750632],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001441353,"about_ca_system_score_gemma":0.00004829089,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004731821,"about_ca_topic_score_gemma":0.0001022054,"domain_scores_codex":[0.9994627,0.00004098898,0.0001736227,0.000122411,0.00007140258,0.0001289324],"domain_scores_gemma":[0.9993008,0.00005432953,0.0000513832,0.0005507406,0.00001780923,0.00002495871],"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":[0.000005822061,0.0006893136,0.0586886,0.0001188274,0.00004366313,0.00002147677,0.006223069,0.0001098942,0.3922192,0.4048816,0.0007498963,0.1362486],"study_design_scores_gemma":[0.0002094589,0.00007143166,0.001991161,0.0001306789,0.000001646976,0.00001610624,0.0001195588,0.003997225,0.939387,0.05343512,0.0004606968,0.0001799708],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.168261,0.0007798241,0.3437245,0.002213039,0.0001421079,0.000133116,9.807599e-7,0.0001597009,0.4845857],"genre_scores_gemma":[0.7902569,0.00001755094,0.209422,0.00004104372,0.000002490187,0.000002281595,2.702666e-7,0.000001769514,0.0002557237],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6219959,"threshold_uncertainty_score":0.1966717,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02488423869270322,"score_gpt":0.2610809197307342,"score_spread":0.2361966810380309,"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."}}