{"id":"W4248548556","doi":"10.1109/iccad.2005.1560094","title":"Incremental partitioning-based vectorless power grid verification","year":2005,"lang":"en","type":"article","venue":"ICCAD-2005. IEEE/ACM International Conference on Computer-Aided Design, 2005.","topic":"Low-power high-performance VLSI design","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Grid; Solver; Power grid; Set (abstract data type); Constraint (computer-aided design); Power (physics); Distributed computing; Parallel computing; Computer engineering; Engineering; Programming language","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":[0.0007094659,0.0006130309,0.000408809,0.0004568821,0.0002915309,0.000668579,0.001498507,0.000374125,0.003135483],"category_scores_gemma":[0.002615819,0.0004237126,0.0005300759,0.0003131217,0.0008258746,0.001382979,0.001332486,0.0007510107,0.0004651689],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006468207,"about_ca_system_score_gemma":0.0008883161,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002681227,"about_ca_topic_score_gemma":0.00315817,"domain_scores_codex":[0.9992524,0.000229698,0.00002725927,0.00008390811,0.0003176886,0.00008897896],"domain_scores_gemma":[0.9986841,0.0006994404,0.00009001696,0.0002841596,0.0002090089,0.00003323644],"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":[0.0001751458,0.000049121,0.0008417009,0.0001541444,0.00002512126,0.0001506113,0.0001273126,0.8615854,0.02089516,0.03952127,0.001703731,0.0747713],"study_design_scores_gemma":[0.00002074434,0.00002967313,0.00006033295,0.000007107539,0.000006232336,0.00002344145,0.00001029102,0.9804978,0.006241838,0.01197747,0.001120516,0.000004601331],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02468559,0.00007009883,0.9705697,0.00007349812,0.00001907368,0.0000615854,0.00008295089,0.001515192,0.002922306],"genre_scores_gemma":[0.7215496,0.0001154879,0.2752869,0.00008701714,0.00001296312,0.0001481645,0.0003385833,0.000351865,0.002109323],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003135483,"threshold_uncertainty_score":0.01048923,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04205902838282014,"score_gpt":0.2645480293589405,"score_spread":0.2224890009761203,"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."}}