{"id":"W2143972262","doi":"10.1117/12.406502","title":"&lt;title&gt;Nonlinear signal processing using index calculus DBNS arithmetic&lt;/title&gt;","year":2000,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Numerical Methods and Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Signal processing; Representation (politics); Digital signal processing; Dynamic range; Range (aeronautics); Arithmetic; Binary number; Algorithm; Speech processing; Speech recognition; Mathematics; Computer hardware; Computer vision","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002968447,0.0003412968,0.0002843067,0.0005464658,0.0003003308,0.001164202,0.0004977306,0.00027844,0.02628124],"category_scores_gemma":[0.0006827087,0.000140871,0.0002308963,0.0007068588,0.0006307157,0.0008107325,0.0006497796,0.00067353,0.00850012],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006275692,"about_ca_system_score_gemma":0.0004048655,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001083027,"about_ca_topic_score_gemma":0.001641236,"domain_scores_codex":[0.9997893,0.0000326755,0.00001176527,0.00002501783,0.0001288504,0.00001241585],"domain_scores_gemma":[0.9998851,0.00002589282,0.00001222372,0.00002754245,0.00004158543,0.000007558768],"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":[0.00008797074,0.00003257076,0.0003748934,0.0003159638,0.00001192232,0.0001811022,0.0001351149,0.01300586,0.05395658,0.558674,0.04038901,0.332835],"study_design_scores_gemma":[0.0000234338,0.00008312142,0.0006575731,0.0001276826,0.00000916613,0.0003148389,0.00005006191,0.2098238,0.03604388,0.1024254,0.650395,0.00004606205],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01004801,0.002898884,0.7891627,0.001119197,0.002828717,0.000125241,0.0004249451,0.001909166,0.1914832],"genre_scores_gemma":[0.1383731,0.005266733,0.677545,0.0006033336,0.0008589271,0.0002653399,0.0008974757,0.001084926,0.1751052],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02628124,"threshold_uncertainty_score":0.08791947,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01527422663653304,"score_gpt":0.2558410371923289,"score_spread":0.2405668105557959,"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."}}