{"id":"W2130194671","doi":"10.5555/1129601.1129654","title":"Incremental partitioning-based vectorless power grid verification","year":2005,"lang":"en","type":"article","venue":"","topic":"Low-power high-performance VLSI design","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Grid; Solver; Power grid; Set (abstract data type); Power (physics); Constraint (computer-aided design); Distributed computing; Computer engineering; Parallel computing; Reliability engineering; 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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0001014307,0.0001255304,0.00009045457,0.00007970256,0.00005942287,0.00004172358,0.00011177,0.00005341418,0.001409806],"category_scores_gemma":[0.000004665676,0.0001254398,0.00003365691,0.0001511743,0.00002168595,0.0003041615,0.000007646277,0.00009144404,0.001186407],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001568792,"about_ca_system_score_gemma":0.00001572816,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008262038,"about_ca_topic_score_gemma":0.00002361375,"domain_scores_codex":[0.9992825,0.00001089913,0.0001827709,0.0001338353,0.0001682194,0.0002217274],"domain_scores_gemma":[0.9996558,0.00001507365,0.00001759376,0.0002237241,0.00002480531,0.00006293725],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0000713758,0.0003676079,0.01440363,0.000141858,0.0001446667,0.000009950585,0.000815005,0.540664,0.2766682,0.005879427,0.1501672,0.01066705],"study_design_scores_gemma":[0.001004339,0.00007361041,0.02632957,0.0000322979,0.000017964,0.000003751352,0.0000497062,0.229648,0.6431807,0.00001452038,0.09911622,0.0005292573],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9416587,0.0001063077,0.02077474,0.0002458476,0.0008327385,0.0002287813,0.00001129507,0.001204936,0.03493666],"genre_scores_gemma":[0.996859,0.000006541214,0.002565383,0.0001820853,0.0001849669,0.0000381681,0.00003351357,0.0000308916,0.00009950718],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3665125,"threshold_uncertainty_score":0.9995913,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006468941962762395,"score_gpt":0.1910788256034644,"score_spread":0.184609883640702,"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."}}