{"id":"W2951735440","doi":"10.48550/arxiv.1503.01147","title":"Random Pulse Train Spectrum Calculation Unleashed","year":2015,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Train; Pulse (music); Spectral density; Blank; Spectrum (functional analysis); SIGNAL (programming language); Pulse wave; Power (physics); Cover (algebra); Mathematics; Statistical physics; Pulse duration; Mathematical analysis; Physics; Algorithm; Computer science; Telecommunications; Optics; Statistics; Engineering; Quantum mechanics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000800963,0.0003119283,0.0003725581,0.0003771883,0.0001016858,0.0002125343,0.001524878,0.0003772405,0.00003310374],"category_scores_gemma":[0.00006091663,0.0003587857,0.0002200607,0.0006090528,0.00008048111,0.0005465126,0.001037582,0.0006016653,0.0001071948],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003124701,"about_ca_system_score_gemma":0.0003837761,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001932785,"about_ca_topic_score_gemma":0.00008508965,"domain_scores_codex":[0.9979192,0.0003873936,0.0002482695,0.000984513,0.0001567507,0.0003038812],"domain_scores_gemma":[0.9979769,0.00007859922,0.0002780284,0.00126692,0.000189431,0.0002101495],"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":[0.0001277812,0.0001693339,0.0005490939,0.00004586545,0.0001088074,0.0002466039,0.001596021,0.3970638,0.00007273927,0.5955542,0.003187719,0.001278013],"study_design_scores_gemma":[0.001313861,0.00005239916,0.0005769568,0.00004184507,0.0000393727,0.000005857582,0.00002905084,0.7973222,0.0004677694,0.1981986,0.001458883,0.0004931508],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06448455,0.00003247984,0.9228361,0.000605519,0.0003327398,0.0004837396,0.000009127588,0.0008766351,0.01033913],"genre_scores_gemma":[0.9925704,0.00003608027,0.004592117,0.0002139348,0.00008106152,0.000001869096,0.00004045565,0.00001985212,0.002444242],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9280858,"threshold_uncertainty_score":0.9998864,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07752239289706081,"score_gpt":0.2104536105091986,"score_spread":0.1329312176121378,"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."}}