{"id":"W4395099382","doi":"10.1007/978-3-031-48679-1_18","title":"On the Optimization of Pippenger’s Bucket Method with Precomputation","year":2023,"lang":"en","type":"book-chapter","venue":"Fields Institute communications","topic":"Cryptography and Residue Arithmetic","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Precomputation; Computer science; Algorithm","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":[0.0003420802,0.0001883625,0.0002161896,0.0002533827,0.00034956,0.00007330445,0.002334665,0.0002144828,0.0000294574],"category_scores_gemma":[0.00006394968,0.0001320783,0.0001165813,0.0002614252,0.0002374133,0.0001772276,0.0005423717,0.0005570928,0.00002991516],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001957154,"about_ca_system_score_gemma":0.0001277806,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003791244,"about_ca_topic_score_gemma":0.0002305242,"domain_scores_codex":[0.9989535,0.00009146347,0.000317075,0.0002612861,0.000260638,0.0001160497],"domain_scores_gemma":[0.9955972,0.001087147,0.0003019639,0.002782075,0.0001954925,0.00003615162],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000003450365,0.0000246192,7.242518e-7,0.00001531899,0.00007248118,0.000001087889,0.0003384588,0.0573986,4.601766e-7,0.9324458,0.001069612,0.008629403],"study_design_scores_gemma":[0.0005234815,0.0004997092,0.0000812092,0.001287933,0.0001953852,0.0000255035,0.00004851293,0.3343189,0.00007710856,0.5944571,0.06770916,0.0007759858],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00000182222,0.0001163661,0.7862284,0.005071119,0.0002042119,0.0003503012,0.00001399843,0.000128464,0.2078854],"genre_scores_gemma":[0.0119357,0.002856142,0.9343943,0.0008982553,0.00007982185,0.0001632563,0.0002992146,0.00007181989,0.04930149],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3379887,"threshold_uncertainty_score":0.5385998,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06329506006541884,"score_gpt":0.2940841703954725,"score_spread":0.2307891103300536,"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."}}