{"id":"W1989144556","doi":"10.1016/j.neucom.2012.07.051","title":"Controlling deterministic output variability in a feature extracting chaotic BAM","year":2013,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Chaotic; Feature (linguistics); Attractor; Computer science; Noise (video); Property (philosophy); Trajectory; Salient; Pattern recognition (psychology); Fixed point; Artificial intelligence; Control theory (sociology); Simple (philosophy); Point (geometry); Algorithm; Control (management); Mathematics; Image (mathematics); 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":[0.0004696158,0.0001749797,0.0002248832,0.00007286729,0.0002091189,0.0003307897,0.0006539243,0.000067599,0.000006185262],"category_scores_gemma":[0.0001818391,0.0001657077,0.00006589059,0.0004515354,0.00002262374,0.000334049,0.0002610896,0.0004512966,0.00005010511],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003093622,"about_ca_system_score_gemma":0.00002880994,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004310328,"about_ca_topic_score_gemma":0.000003845299,"domain_scores_codex":[0.99827,0.0001696968,0.0003381187,0.0005860991,0.0001684307,0.0004676435],"domain_scores_gemma":[0.9980618,0.001139239,0.0001553597,0.0004762426,0.00005980156,0.0001075627],"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":[0.000004248568,0.000228724,0.0163394,0.00006156052,0.000009655309,0.00007410457,0.0006419401,0.03200353,0.007166045,0.01629151,0.0003897836,0.9267895],"study_design_scores_gemma":[0.0002378116,0.00002086765,0.03919721,0.00003821907,0.000002563102,0.00003376402,0.000006157999,0.9578109,0.00009674269,0.001908629,0.0004867015,0.0001604694],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4307293,0.0000368353,0.5639607,0.002586449,0.0003818204,0.0007704481,4.058045e-7,0.0003211399,0.00121298],"genre_scores_gemma":[0.9722602,0.00000108859,0.02663407,0.0007845152,0.0002105613,0.00004844349,6.277758e-7,0.00001298306,0.0000474708],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.926629,"threshold_uncertainty_score":0.6757364,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.015109885731958,"score_gpt":0.2404814259736598,"score_spread":0.2253715402417018,"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."}}