{"id":"W1993619022","doi":"10.1007/s00521-006-0064-8","title":"Modified multi-layered perceptron applied to packing and covering problems","year":2006,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Convergence (economics); Computational Science and Engineering; Limit (mathematics); Multilayer perceptron; Mathematical optimization; Perceptron; Artificial intelligence; Algorithm; Artificial neural network; Machine learning; 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":[0.00009619013,0.000168999,0.0001592592,0.00006294352,0.0005809963,0.0002935967,0.0003057999,0.00004512984,4.619793e-7],"category_scores_gemma":[0.000002286158,0.0001643268,0.00002688796,0.0003764094,0.00004365484,0.0001003587,0.000316874,0.000148553,0.000008184224],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001589175,"about_ca_system_score_gemma":0.000007873507,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006941152,"about_ca_topic_score_gemma":0.00001495007,"domain_scores_codex":[0.9987553,0.00001683986,0.0002438641,0.000566538,0.0001161214,0.0003012946],"domain_scores_gemma":[0.9993293,0.00008469023,0.00007895612,0.0003438734,0.00003749292,0.0001256382],"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":[0.000004610495,0.0001707334,0.001678271,0.00006124371,0.00001142487,0.000001193267,0.0004417292,0.2447626,0.06298707,0.3324572,0.0004597752,0.3569641],"study_design_scores_gemma":[0.0004378085,0.00003161839,0.01708529,0.00002162776,0.000008419088,0.0000246428,0.00003397974,0.9729027,0.000443793,0.003841671,0.004791528,0.0003768925],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2110402,0.00009331508,0.7862687,0.001042761,0.00002522735,0.0006431752,0.000002909104,0.0002875543,0.0005961633],"genre_scores_gemma":[0.9674776,0.000008163568,0.0317761,0.0003357607,0.000171091,0.0001493013,0.000007217802,0.00001358059,0.00006120065],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7564374,"threshold_uncertainty_score":0.6701052,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02096474775715191,"score_gpt":0.254700122397957,"score_spread":0.2337353746408051,"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."}}