{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00005475246,0.0001402915,0.0001379114,0.00006319912,0.00002701247,0.00003440374,0.0001045236,0.00005942558,0.002859266],"category_scores_gemma":[0.00000357291,0.0001311018,0.00004286887,0.0000975696,0.000009741527,0.00006200348,0.00001679492,0.0001048855,0.0003004074],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000066236,"about_ca_system_score_gemma":0.000005851496,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001403568,"about_ca_topic_score_gemma":0.000001897051,"domain_scores_codex":[0.9993159,0.000007430346,0.0001584274,0.0001268228,0.0001566815,0.0002347954],"domain_scores_gemma":[0.9996476,0.00002037682,0.00001167824,0.0001869218,0.00001455718,0.0001188539],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000002454178,0.00002876839,0.00001286174,0.00004752926,0.00001569315,0.00004466845,0.0002190104,0.0002705772,0.001917697,0.0001166856,0.79634,0.200984],"study_design_scores_gemma":[0.000993997,0.0004277331,0.00002679893,0.00004787948,0.00004669244,0.00003211747,0.00009029505,0.2545848,0.1406304,0.0009585771,0.601387,0.0007736592],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0004012776,0.0008886005,0.8752143,0.00005826961,0.0001400695,0.0001825526,0.00001256613,0.001601111,0.1215012],"genre_scores_gemma":[0.6049311,0.001309303,0.3213305,0.0003385692,0.0005164111,0.00006383679,0.0000143911,0.0001765176,0.07131935],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6045299,"threshold_uncertainty_score":0.9980522,"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."}}