{"id":"W2950634431","doi":"10.48550/arxiv.1008.1366","title":"Efficient Dealiased Convolutions without Padding","year":2010,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Digital Filter Design and Implementation","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Padding; Computer science; Convolution (computer science); Decoupling (probability); Computation; Fast Fourier transform; Fourier transform; Parallel computing; Computational science; Algorithm; Mathematics; Mathematical analysis","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001950012,0.0002490159,0.0002176149,0.0002519171,0.0001863379,0.0002886726,0.001288851,0.0001842669,0.00004244604],"category_scores_gemma":[0.00002612791,0.0002905135,0.000159341,0.000397012,0.00008772893,0.0002379413,0.001169607,0.0004666244,0.0001599704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001579412,"about_ca_system_score_gemma":0.000213511,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001037314,"about_ca_topic_score_gemma":0.00003820275,"domain_scores_codex":[0.9984263,0.00007803995,0.000196222,0.0008417298,0.0001082495,0.0003495203],"domain_scores_gemma":[0.9984515,0.00006609869,0.0002057015,0.0009484955,0.0001442158,0.0001839618],"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":[0.00002257951,0.0002228721,0.003002321,0.0000690235,0.0001103139,0.0001484358,0.0005080915,0.1587066,0.00128702,0.8324804,0.001228915,0.002213399],"study_design_scores_gemma":[0.0004986782,0.00003902596,0.001306629,0.00005185867,0.00004179243,0.000005283107,0.0000441574,0.9778156,0.000518255,0.01811477,0.001136096,0.0004278411],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2343085,0.000007126828,0.7596403,0.00006565204,0.0007570466,0.0002704572,0.00003297407,0.0002567104,0.004661136],"genre_scores_gemma":[0.9932555,0.000006026033,0.005391979,0.0001192323,0.00005234983,0.000001705324,0.00004314589,0.00001354843,0.001116505],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.819109,"threshold_uncertainty_score":0.9999547,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1026362704294891,"score_gpt":0.2147010992791457,"score_spread":0.1120648288496566,"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."}}