{"id":"W1750273152","doi":"10.1109/iscas.1993.394055","title":"Circuit partitioning using a Tabu search approach","year":2002,"lang":"en","type":"article","venue":"1993 IEEE International Symposium on Circuits and Systems","topic":"VLSI and FPGA Design Techniques","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Tabu search; Netlist; Simulated annealing; Guided Local Search; Hill climbing; Computer science; Mathematical optimization; Job shop scheduling; Algorithm; Theoretical computer science; Mathematics; Schedule","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":[0.0002453161,0.0001922923,0.0002118801,0.0001503139,0.0001192283,0.0002414333,0.000195419,0.0001174581,0.00005314437],"category_scores_gemma":[0.000005475154,0.0001913856,0.00005956294,0.0001222627,0.00003547666,0.0002052459,0.00001285495,0.0001995593,0.00004251705],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001576755,"about_ca_system_score_gemma":0.000004571578,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000888254,"about_ca_topic_score_gemma":9.792974e-7,"domain_scores_codex":[0.9986631,0.00004393108,0.0003241464,0.0002758367,0.0004167012,0.0002762411],"domain_scores_gemma":[0.9995524,0.00004907702,0.00004304711,0.0001740579,0.00007491263,0.0001064934],"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":[0.0000116341,0.0005542712,0.00833828,0.0009600313,0.0009004881,0.0001377313,0.005546798,0.3614216,0.543219,0.0437225,0.01366518,0.0215225],"study_design_scores_gemma":[0.0003246269,0.00005713344,0.0001625141,0.0002019717,0.00001626558,0.0002149881,0.0001161698,0.9901633,0.003544992,0.00005779979,0.004777967,0.0003622776],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5028975,0.001906436,0.2023724,0.000133291,0.004816778,0.001117055,0.0001486336,0.001545776,0.2850621],"genre_scores_gemma":[0.9984671,0.0001915252,0.00004956617,0.00004143949,0.0005682271,0.00005795399,0.00001323423,0.00004353922,0.0005674282],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6287417,"threshold_uncertainty_score":0.780448,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08049535451018379,"score_gpt":0.2574485584508043,"score_spread":0.1769532039406205,"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."}}