{"id":"W562550760","doi":"10.1049/el.2015.0342","title":"Area efficient floating‐point FFT butterfly architectures based on multi‐operand adders","year":2015,"lang":"en","type":"article","venue":"Electronics Letters","topic":"Numerical Methods and Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Operand; Adder; Fast Fourier transform; Computer science; Parallel computing; Floating point; Butterfly; Point (geometry); Arithmetic; Computer architecture; Computer hardware; Algorithm; Mathematics; Telecommunications; Latency (audio)","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":[0.0006819913,0.0002815317,0.0002595195,0.0001592918,0.0001390755,0.0001764644,0.000798912,0.00005526451,0.000009246142],"category_scores_gemma":[0.0001713555,0.0002278745,0.0001290395,0.0004101517,0.00006835667,0.00005247386,0.0001048961,0.0004803679,0.00003763158],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001859532,"about_ca_system_score_gemma":0.000140419,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002806076,"about_ca_topic_score_gemma":0.000004215447,"domain_scores_codex":[0.9975368,0.0002632911,0.0002556826,0.0006192476,0.0005388666,0.000786053],"domain_scores_gemma":[0.9986971,0.000240767,0.00009647199,0.0006392512,0.00005396231,0.0002724729],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001769299,0.0007369425,0.0002723344,0.00003012379,0.0000995124,0.000162055,0.00244865,0.6884189,0.0767853,0.002564514,0.009787894,0.2185168],"study_design_scores_gemma":[0.001313403,0.0004390842,0.00009712306,0.00002554117,0.000007766501,0.00001038202,0.00001332149,0.9788778,0.01309772,0.0003803218,0.005361559,0.0003759202],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0484489,0.0001605823,0.9355308,0.0148378,0.000393475,0.0002245686,0.000002289791,0.0001966331,0.0002049863],"genre_scores_gemma":[0.4042521,0.000001875063,0.5712095,0.02429876,0.0001467451,0.00002294614,0.000003812128,0.00003221674,0.00003199761],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3643213,"threshold_uncertainty_score":0.9292451,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02283817342132538,"score_gpt":0.2606227942640772,"score_spread":0.2377846208427518,"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."}}