{"id":"W1983992983","doi":"10.1002/cta.332","title":"A hybrid evolutionary analogue module placement algorithm for integrated circuit layout designs","year":2005,"lang":"en","type":"article","venue":"International Journal of Circuit Theory and Applications","topic":"VLSI and FPGA Design Techniques","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"KU Leuven; Carnegie Mellon University","keywords":"Simulated annealing; Algorithm; Computer science; Integrated circuit layout; Placement; Representation (politics); Genetic algorithm; Electronic circuit; Integrated circuit; Circuit design; Physical design; Embedded system; 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":[0.0005132781,0.0001347434,0.0001628313,0.0002211327,0.0000768948,0.00005035903,0.0003243575,0.00005411667,0.00007008341],"category_scores_gemma":[0.00002602928,0.0001294955,0.0001049714,0.00007268315,0.00005877727,0.00028324,0.00001552917,0.000168504,0.000008704798],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001612144,"about_ca_system_score_gemma":0.00004742287,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":9.632438e-7,"about_ca_topic_score_gemma":5.711237e-7,"domain_scores_codex":[0.9990807,0.00003939267,0.0004149693,0.0001211443,0.0002010287,0.0001427759],"domain_scores_gemma":[0.9991229,0.0001998913,0.0001252799,0.0001109768,0.0003509934,0.00008996185],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00002706169,0.0001462019,0.00002939622,0.00001245236,0.0003470727,0.000005258219,0.0001582953,0.003699189,0.006659478,0.4460593,0.002789465,0.5400668],"study_design_scores_gemma":[0.001708283,0.0002103163,0.0003245481,0.0001451811,0.0001693124,0.0009018109,0.0003192504,0.03495488,0.02099322,0.7235535,0.216164,0.0005556219],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002037176,0.000861604,0.9949926,0.00004924796,0.0001229417,0.0002999378,0.0002210205,0.00009581038,0.001319684],"genre_scores_gemma":[0.9925522,0.0002991997,0.005804596,0.0001516422,0.0007510127,0.0001942198,0.00007461536,0.00002675069,0.0001457792],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.990515,"threshold_uncertainty_score":0.5280671,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01838131401153997,"score_gpt":0.2563521510000683,"score_spread":0.2379708369885283,"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."}}