{"id":"W2133761062","doi":"10.1109/iscas.2007.378191","title":"Two Clustering Preprocessing Techniques for Large-Scale Circuits","year":2007,"lang":"en","type":"article","venue":"","topic":"VLSI and FPGA Design Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Cluster analysis; Preprocessor; Computer science; Benchmark (surveying); Electronic circuit; Data mining; Data pre-processing; Scale (ratio); Algorithm; 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":[0.0005276919,0.0001393665,0.0001424194,0.0001137311,0.00007454819,0.00004539973,0.0001339772,0.00009322193,0.00002837809],"category_scores_gemma":[0.00001221496,0.0001382713,0.00005776608,0.0001259406,0.000009668572,0.0001831824,0.00002710911,0.00009484512,0.000005459113],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005139069,"about_ca_system_score_gemma":0.000006280467,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004242484,"about_ca_topic_score_gemma":0.00005856648,"domain_scores_codex":[0.9991137,0.000003489538,0.0002277869,0.0001773189,0.00008751707,0.0003901791],"domain_scores_gemma":[0.9996365,0.00004380032,0.00002003204,0.0001899593,0.00004568751,0.00006399828],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001226563,0.00006443017,0.0008202693,0.0006179294,0.00003771407,0.000008785497,0.0009874149,0.0002045656,0.3871803,0.0014489,0.004860103,0.6037573],"study_design_scores_gemma":[0.0002283691,0.00004187177,0.0001156727,0.000079217,0.00001085999,0.00001523173,0.0001512201,0.03461932,0.9464546,0.001314463,0.01664588,0.0003232282],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002928923,0.0001407926,0.9201732,0.000007835348,0.00008053464,0.0003369095,0.000003915281,0.003657423,0.07267042],"genre_scores_gemma":[0.8489698,0.0000132376,0.1502529,0.00009002239,0.000175041,0.0000667004,0.000004998445,0.00005400306,0.000373252],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8460409,"threshold_uncertainty_score":0.5638538,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0153265288273615,"score_gpt":0.2738462145749965,"score_spread":0.258519685747635,"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."}}