{"id":"W1549861330","doi":"10.1007/3-540-44495-5_19","title":"On Efficient Normal Basis Multiplication","year":2000,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Coding theory and cryptography","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Multiplication (music); Computer science; Normal basis; Cryptography; Finite field; Basis (linear algebra); Multiplication algorithm; Arithmetic; Field (mathematics); Class (philosophy); Theoretical computer science; Algorithm; Mathematics; Discrete mathematics; Artificial intelligence; Galois theory","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":[0.0007568228,0.0004675543,0.000348986,0.001084601,0.0003467036,0.0004028461,0.003167847,0.000252078,0.00008690824],"category_scores_gemma":[0.00004220334,0.0004297086,0.0001937674,0.0008046953,0.0005057615,0.0002350322,0.0005819564,0.000677633,0.000201337],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001632316,"about_ca_system_score_gemma":0.0001882125,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009467575,"about_ca_topic_score_gemma":0.00001191543,"domain_scores_codex":[0.9965883,0.00004252333,0.0003949299,0.00150727,0.0008881507,0.0005788594],"domain_scores_gemma":[0.9972666,0.0005811264,0.0001814387,0.001693647,0.0001112025,0.000165998],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001400158,0.00004632014,0.000005445156,0.000008035139,0.000005158764,0.00001873953,0.0003144973,0.07788002,0.00004400712,0.1728477,0.00001491406,0.7488012],"study_design_scores_gemma":[0.0002895763,0.0002880755,0.0001558353,0.0002966308,0.000007084113,0.00003351362,4.163558e-8,0.5168937,0.00169523,0.4771388,0.002482235,0.0007192867],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0005260038,0.0001106961,0.9830898,0.0005151457,0.0008639736,0.0003098283,0.000005540818,0.0002243264,0.01435465],"genre_scores_gemma":[0.785993,0.0000454517,0.2087497,0.00435514,0.0005032361,0.00002656118,0.000008018987,0.00005190519,0.0002669358],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7854671,"threshold_uncertainty_score":0.9998155,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01035937227455769,"score_gpt":0.2218706764179062,"score_spread":0.2115113041433485,"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."}}