{"id":"W2054507989","doi":"10.1007/s10470-008-9135-3","title":"Robust low-sensitivity Adaline neuron based on Continuous Valued Number System","year":2008,"lang":"en","type":"article","venue":"Analog Integrated Circuits and Signal Processing","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary; University of Windsor","funders":"","keywords":"Computer science; Sensitivity (control systems); Artificial neural network; Redundancy (engineering); Modular design; Analogue electronics; Electronic engineering; Control theory (sociology); Algorithm; Electronic circuit; Artificial intelligence; Engineering; Electrical 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":[0.0002460862,0.000243045,0.0002987868,0.00007780248,0.000584428,0.0002253129,0.0002412629,0.00009364865,0.000008973931],"category_scores_gemma":[0.00001410313,0.0001925368,0.00006560245,0.0006612386,0.0001040563,0.0002941687,0.00003481132,0.0003302783,0.0000175122],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003912699,"about_ca_system_score_gemma":0.0001300788,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009609103,"about_ca_topic_score_gemma":0.000009567845,"domain_scores_codex":[0.9983652,0.0001347626,0.0003012709,0.0005754728,0.0002789027,0.0003443595],"domain_scores_gemma":[0.9990731,0.0001154482,0.0001558919,0.0002551153,0.0002451148,0.0001553172],"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":[0.00009852742,0.001311764,0.009401798,0.000615893,0.0001046841,0.002736808,0.0009341994,0.1118356,0.03423426,0.04530761,0.005706605,0.7877123],"study_design_scores_gemma":[0.0003329417,0.00008187108,0.001802524,0.0002682671,0.00001332239,0.0002305787,0.00003818621,0.9956229,0.00108151,0.00007577919,0.0001957463,0.0002563302],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1430167,0.00009459313,0.8528647,0.0002800404,0.00007171496,0.0002004949,0.00000786643,0.0003247703,0.003139066],"genre_scores_gemma":[0.9979961,0.000006853604,0.0008091117,0.0009317381,0.0001035235,0.00001470492,0.00001522493,0.00001745343,0.0001052862],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8837874,"threshold_uncertainty_score":0.7851422,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0255424262207684,"score_gpt":0.2228473947583902,"score_spread":0.1973049685376217,"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."}}