{"id":"W1929609097","doi":"10.1109/dft.2015.7315159","title":"Approximate compressors for error-resilient multiplier design","year":2015,"lang":"en","type":"article","venue":"","topic":"Low-power high-performance VLSI design","field":"Engineering","cited_by":149,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Multiplier (economics); Sharpening; Computer science; Adder; Algorithm; Approximation theory; Approximation error; Gas compressor; Multiplication (music); Mathematics; Artificial intelligence; Engineering","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":[0.0003313588,0.0002344899,0.0002294671,0.000107981,0.00005234809,0.00004639923,0.0002592131,0.0001005024,0.00005003073],"category_scores_gemma":[0.00003108734,0.0002022447,0.00006041532,0.0001440595,0.00003656382,0.0002408808,0.00003462766,0.0001030806,0.0002817983],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001060242,"about_ca_system_score_gemma":0.00002578876,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000069212,"about_ca_topic_score_gemma":0.000002808107,"domain_scores_codex":[0.998805,0.00002117321,0.0002601467,0.000223246,0.0002224517,0.0004679943],"domain_scores_gemma":[0.9992375,0.00008922417,0.00002485887,0.0003537944,0.00008312472,0.0002115324],"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":[0.00008606377,0.00006281077,0.0001987221,0.0001058044,0.00006974282,0.000003667835,0.0009463283,0.8595631,0.006712248,0.0007502912,0.1293362,0.002165032],"study_design_scores_gemma":[0.001383556,0.00008854458,0.00009869206,0.00001420343,0.00001772237,0.00000371189,0.0001215387,0.9194751,0.05789893,0.0002756495,0.02028158,0.0003407912],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03828645,0.0002112239,0.9501663,0.00004890239,0.0008836958,0.001079268,0.00001085553,0.001140501,0.00817282],"genre_scores_gemma":[0.8692937,0.000007973253,0.128686,0.00007101776,0.0001124667,0.0002715982,0.00001280893,0.00009148165,0.001452891],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8310073,"threshold_uncertainty_score":0.8247298,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06762559117701004,"score_gpt":0.2579860799468495,"score_spread":0.1903604887698395,"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."}}