{"id":"W2111524252","doi":"10.1109/cicc.1993.590685","title":"A combined eigenvector tabu search approach for circuit partitioning","year":2002,"lang":"en","type":"article","venue":"","topic":"VLSI and FPGA Design Techniques","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Tabu search; Guided Local Search; Eigenvalues and eigenvectors; Partition (number theory); Mathematical optimization; Computer science; Algorithm; Iterative method; Search algorithm; Mathematics","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":[0.0001004503,0.00009514925,0.000112988,0.00006065187,0.00006337972,0.0000383243,0.0000955559,0.00006397066,0.0004887129],"category_scores_gemma":[0.000007686729,0.00009304939,0.00005410029,0.0001154963,0.0000153297,0.00008327763,0.000008714541,0.00008254798,0.00004167952],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003142238,"about_ca_system_score_gemma":0.000002150323,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005520899,"about_ca_topic_score_gemma":4.555293e-7,"domain_scores_codex":[0.9994057,0.000008746789,0.000123299,0.0001216177,0.00008887763,0.0002517002],"domain_scores_gemma":[0.999731,0.00003019918,0.000006108863,0.0001462096,0.00002652986,0.00005997515],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003218628,0.0008774232,0.004348197,0.001771047,0.0005440029,0.00001792104,0.004402799,0.02668546,0.2405226,0.1338689,0.4516558,0.1352737],"study_design_scores_gemma":[0.0004191967,0.0001094549,0.0001594377,0.00001041153,0.00001200009,0.000004161819,0.00006338224,0.9235094,0.07178688,0.0008579115,0.002789337,0.0002783933],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00446041,0.0001346563,0.9242613,0.000017842,0.00004390533,0.0003963105,0.000007341353,0.001321541,0.06935667],"genre_scores_gemma":[0.9780801,0.00002842724,0.02016168,0.00004314401,0.00007284121,0.0002171874,0.00001799027,0.00003345772,0.001345156],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9736197,"threshold_uncertainty_score":0.5351063,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06031010982164158,"score_gpt":0.2190760474328658,"score_spread":0.1587659376112242,"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."}}