{"id":"W2163062501","doi":"10.1038/srep12858","title":"Optimal nonlinear information processing capacity in delay-based reservoir computers","year":2015,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Neural Networks and Reservoir Computing","field":"Computer Science","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Agence Nationale de la Recherche; Ottawa Hospital Research Institute; Schlumberger Foundation","keywords":"Reservoir computing; Computer science; Construct (python library); Information processing; Focus (optics); Architecture; Scheme (mathematics); Nonlinear system; Function (biology); Reservoir modeling; Distributed computing; Data processing; Reservoir simulation; Artificial intelligence; Computer engineering; Machine learning; Artificial neural network; Database; Computer network; Petroleum engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004811972,0.0002018198,0.0005278843,0.0003453877,0.0003624829,0.0009861097,0.000712967,0.0007363787,0.001519296],"category_scores_gemma":[0.002099253,0.0002453096,0.0002211638,0.0003066527,0.001536656,0.001538036,0.0008779818,0.000550354,0.0001651312],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000823055,"about_ca_system_score_gemma":0.000546451,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000777831,"about_ca_topic_score_gemma":0.0005523631,"domain_scores_codex":[0.9998485,0.00004635916,0.000008078002,0.00002501459,0.00003667,0.00003530147],"domain_scores_gemma":[0.9993653,0.0004286817,0.00006196056,0.00004751479,0.00005725745,0.00003929865],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000187317,0.00006379215,0.0004296456,0.0001924993,0.00002379512,0.0001832143,0.0001518753,0.6006405,0.02513254,0.3589872,0.0009335398,0.01307407],"study_design_scores_gemma":[0.000007841189,0.00001419933,0.00007978304,0.000008732575,0.000003590338,0.00001717565,0.000008675597,0.9602259,0.001567803,0.03775017,0.0003051478,0.00001101412],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.562578,0.00273371,0.3993872,0.00163161,0.0001130985,0.00005936399,0.0001758043,0.000329375,0.0329919],"genre_scores_gemma":[0.9905094,0.0003562375,0.007464138,0.00004079983,0.00001378038,0.00003566676,0.00001902585,0.00001885976,0.001542087],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001519296,"threshold_uncertainty_score":0.00597167,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04307207561179538,"score_gpt":0.25326228023116,"score_spread":0.2101902046193646,"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."}}