{"id":"W2591663199","doi":"10.1103/physics.10.12","title":"Reservoir Computing Speeds Up","year":2017,"lang":"en","type":"article","venue":"Physics","topic":"Neural Networks and Reservoir Computing","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Agence Nationale de la Recherche; Ottawa Hospital Research Institute","keywords":"Computer science; Environmental science","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":[0.0004070315,0.0008658878,0.0007612399,0.0005406381,0.0003861663,0.00156847,0.0009676763,0.0008789243,0.01831004],"category_scores_gemma":[0.003765156,0.0003362313,0.0003590267,0.0005782389,0.0006231412,0.004241078,0.001329611,0.001488996,0.004846396],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005405059,"about_ca_system_score_gemma":0.0008153139,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008358529,"about_ca_topic_score_gemma":0.0007804456,"domain_scores_codex":[0.9995621,0.00005782229,0.00003159996,0.0001124217,0.0001715134,0.00006459056],"domain_scores_gemma":[0.9986249,0.0007786931,0.00006651018,0.000263794,0.00021322,0.0000528566],"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":[0.0007683161,0.0001996623,0.001511257,0.001035723,0.0001112854,0.0005003873,0.0004380264,0.1267058,0.09481341,0.2882681,0.03508458,0.4505635],"study_design_scores_gemma":[0.0000528722,0.0001583795,0.0004469701,0.00009039774,0.00006612209,0.000312835,0.00008192592,0.8226102,0.04987007,0.0744781,0.05178203,0.00005008814],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1572626,0.00561655,0.7341653,0.004842208,0.001987168,0.0001687322,0.000947539,0.01190703,0.08310282],"genre_scores_gemma":[0.829267,0.002874972,0.137294,0.0006036478,0.0003906765,0.0001908244,0.0007101743,0.001138373,0.02753028],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01831004,"threshold_uncertainty_score":0.06125319,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05265945637261005,"score_gpt":0.3056341845562905,"score_spread":0.2529747281836804,"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."}}