{"id":"W2468710943","doi":"10.7567/ssdm.2008.d-3-3","title":"Reset Level Boosting in Self-Adaptive APS for Wide Output Swing at a Low Voltage Operation","year":2008,"lang":"en","type":"article","venue":"","topic":"Low-power high-performance VLSI design","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Reset (finance); Boosting (machine learning); Swing; Materials science; Voltage; Electronic engineering; Electrical engineering; Optoelectronics; Computer science; Artificial intelligence; Engineering; Mechanical 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001409628,0.0001512602,0.0001589265,0.0001857186,0.0002552324,0.0004181688,0.000737625,0.0002802417,0.002531102],"category_scores_gemma":[0.0003915501,0.0001622644,0.0001364357,0.000186362,0.0001589804,0.0004450003,0.0003969008,0.0003406938,0.0005436626],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002246855,"about_ca_system_score_gemma":0.0001365288,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001845446,"about_ca_topic_score_gemma":0.0006651534,"domain_scores_codex":[0.9999067,0.00001182022,0.000006592746,0.00002300515,0.00003545333,0.00001652561],"domain_scores_gemma":[0.9998263,0.00005317988,0.00002416404,0.00003536643,0.00004177494,0.00001926237],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003938222,0.00008583823,0.001885094,0.00008483511,0.00002566916,0.000258523,0.0002455206,0.00654385,0.9038993,0.007575638,0.0009377092,0.07806433],"study_design_scores_gemma":[0.00004391211,0.0006865307,0.004130234,0.00003448072,0.00006719485,0.001200674,0.00009727094,0.259193,0.7240762,0.004002444,0.006439633,0.00002845152],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7395599,0.0006770095,0.2428489,0.0003373199,0.0001334264,0.00006470776,0.0001316704,0.002170633,0.01407653],"genre_scores_gemma":[0.9869987,0.00005103457,0.01065327,0.00006714166,0.00001927628,0.000008965028,0.00002627997,0.00004838948,0.002126826],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002531102,"threshold_uncertainty_score":0.008467436,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04129902449785514,"score_gpt":0.2194648479514302,"score_spread":0.1781658234535751,"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."}}