{"id":"W4292633129","doi":"10.1007/978-3-030-98347-5_6","title":"Majority Logic-Based Approximate Multipliers for Error-Tolerant Applications","year":2022,"lang":"en","type":"book-chapter","venue":"","topic":"Low-power high-performance VLSI design","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan; University of Alberta","funders":"","keywords":"Multiplier (economics); Computer science; Electronic circuit; Algorithm; Artificial neural network; Arithmetic; Mathematics; Artificial intelligence; Engineering; Electrical 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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002143487,0.0005873549,0.0005411371,0.0002821254,0.0002484584,0.00004554854,0.0005083371,0.0003607448,0.002363594],"category_scores_gemma":[0.000004402747,0.0005949669,0.0002915388,0.00007226997,0.00007749025,0.0001129639,0.00006701476,0.0005679369,0.0002427945],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003817011,"about_ca_system_score_gemma":0.00007566094,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006365156,"about_ca_topic_score_gemma":0.000008663576,"domain_scores_codex":[0.9981214,0.000006800326,0.0005014886,0.0005449538,0.0003387499,0.000486597],"domain_scores_gemma":[0.9987332,0.0001239696,0.000104407,0.0008206288,0.00007317208,0.0001445977],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001088539,0.0001221389,0.00001551587,0.002500886,0.0005799273,0.00001965304,0.0002070092,0.2899711,0.0006804627,0.6549538,0.03713405,0.01370659],"study_design_scores_gemma":[0.0007477119,0.00005765844,0.000002920465,0.00002856182,0.0001003799,0.00000322338,0.00001335764,0.2309528,0.0003529905,0.004426898,0.7624903,0.0008231963],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.00001080426,0.0004571776,0.481421,0.00009758351,0.0006351609,0.003325354,0.0006065913,0.00180845,0.5116379],"genre_scores_gemma":[0.03870234,0.0005940697,0.2694547,0.001851438,0.001845096,0.02342051,0.00637444,0.002393745,0.6553637],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.7253563,"threshold_uncertainty_score":0.9996502,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02408923146320867,"score_gpt":0.2259186012384266,"score_spread":0.2018293697752179,"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."}}