{"id":"W2156791867","doi":"10.1023/a:1021726007566","title":"The Minimum Number of Errors in the N-Parity and its Solution with an Incremental Neural Network","year":2002,"lang":"en","type":"article","venue":"Neural Processing Letters","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Artificial neural network; Computational intelligence; Constructive; Feedforward neural network; Perceptron; Parity (physics); Activation function; Computer science; Multilayer perceptron; Algorithm; Artificial intelligence; Mathematics; Pattern recognition (psychology); Physics","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":[0.0001986677,0.0001147677,0.00009605369,0.00001427419,0.0004607893,0.0002007443,0.0005472006,0.0000241057,0.000001467516],"category_scores_gemma":[0.00000461734,0.00006394633,0.00001929871,0.0004082884,0.0001256022,0.0005500481,0.00007737092,0.000210251,0.000001283149],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001032722,"about_ca_system_score_gemma":0.000005903017,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000218122,"about_ca_topic_score_gemma":0.00006118529,"domain_scores_codex":[0.998943,0.0001071712,0.0001809845,0.0002490282,0.0002242074,0.0002955837],"domain_scores_gemma":[0.9995189,0.00007808801,0.0001173335,0.000224265,0.00002268113,0.00003874571],"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":[0.0003405496,0.001040606,0.1114624,0.0003837983,0.00007323001,0.0001652343,0.02246742,0.05111707,0.0566368,0.01531088,0.03425143,0.7067506],"study_design_scores_gemma":[0.0002541334,0.00006594858,0.01273715,0.000028413,0.000007408787,0.00009608134,0.00007423123,0.9860041,0.0001241175,0.0002330402,0.0002342858,0.0001411276],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9762238,0.0002134915,0.0008513762,0.0223556,0.00005770197,0.0001852996,4.251651e-7,0.00003336834,0.00007900001],"genre_scores_gemma":[0.9957235,0.00001026251,0.0008546853,0.003272578,0.0001000161,0.00002295025,9.421799e-7,0.000006204436,0.00000884693],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.934887,"threshold_uncertainty_score":0.3544065,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0271416715162606,"score_gpt":0.2593501361350556,"score_spread":0.232208464618795,"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."}}