{"id":"W2113502461","doi":"10.1109/edac.1993.386479","title":"ML-Germinal: A new heuristic standard cell placement algorithm","year":2002,"lang":"en","type":"article","venue":"","topic":"VLSI and FPGA Design Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Heuristic; Convergence (economics); Algorithm; Computer science; Point (geometry); Mathematical optimization; Mathematics; Artificial intelligence","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":[0.0002117183,0.0005491839,0.0006702543,0.001081993,0.0004509253,0.001100773,0.001690637,0.0008238884,0.007651973],"category_scores_gemma":[0.0006668888,0.0003164122,0.0005639134,0.0009978091,0.0003299797,0.0008027033,0.001001681,0.0006591938,0.002244418],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007232775,"about_ca_system_score_gemma":0.0009973006,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003313391,"about_ca_topic_score_gemma":0.007011222,"domain_scores_codex":[0.9997104,0.00003760192,0.00001593247,0.00005081498,0.0001459555,0.00003925022],"domain_scores_gemma":[0.9998099,0.00003505173,0.00002280729,0.00004830307,0.00006444516,0.00001955544],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002842589,0.00009673035,0.0005106907,0.000194115,0.00005643496,0.0001254525,0.0000731433,0.2362666,0.0236287,0.014407,0.0161831,0.7081739],"study_design_scores_gemma":[0.00007592174,0.0001577555,0.0003065806,0.00001897198,0.00001980729,0.0001577812,0.00002742177,0.959664,0.01372456,0.005467523,0.02035552,0.00002417896],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01333525,0.0002831423,0.9772754,0.0001201324,0.0001148838,0.00006685152,0.0001629871,0.003081176,0.005560193],"genre_scores_gemma":[0.1414853,0.000190058,0.848866,0.0001697396,0.00006206806,0.0001289273,0.0006189389,0.0004452541,0.008033725],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007651973,"threshold_uncertainty_score":0.02559847,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0116265762457624,"score_gpt":0.1951556976369745,"score_spread":0.1835291213912121,"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."}}