{"id":"W4387014679","doi":"10.1103/physrevresearch.5.033213","title":"Catch-22s of reservoir computing","year":2023,"lang":"en","type":"article","venue":"Physical Review Research","topic":"Neural Networks and Reservoir Computing","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reservoir computing; Attractor; Trajectory; Focus (optics); Computer science; Nonlinear system; Transient (computer programming); Key (lock); Simple (philosophy); Dynamical systems theory; Artificial intelligence; Applied mathematics; Artificial neural network; Mathematics; Recurrent neural network; Physics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003158191,0.0001400152,0.0004577927,0.000169599,0.0002653389,0.00007865232,0.002164896,0.00002825966,0.000006417178],"category_scores_gemma":[0.0006027494,0.0001049276,0.000202864,0.004811335,0.0001454772,0.0001935307,0.002533672,0.0006393325,0.0004048821],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000309094,"about_ca_system_score_gemma":0.00009924722,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000533135,"about_ca_topic_score_gemma":0.000002100264,"domain_scores_codex":[0.9963087,0.0006552914,0.0003865331,0.0005231995,0.001308728,0.0008175499],"domain_scores_gemma":[0.9966552,0.00164944,0.00009795906,0.000985307,0.0004221894,0.0001898668],"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":[0.00001728349,0.000675212,0.001117657,0.01173687,0.0001096328,0.0002886517,0.001223189,0.008154608,0.0102343,0.2166294,0.2323888,0.5174243],"study_design_scores_gemma":[0.0002232808,0.0002473337,0.002325671,0.004809029,0.000006904905,0.000008201667,0.00002131033,0.9322227,0.001600816,0.02633747,0.03189257,0.000304668],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8375033,0.06362864,0.02274079,0.04711489,0.001094266,0.00343418,0.000007953891,0.001641372,0.02283466],"genre_scores_gemma":[0.9909193,0.00669602,0.001370121,0.0001951552,0.0004321166,0.00001714959,0.000003893764,0.00001929835,0.0003469763],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9240682,"threshold_uncertainty_score":0.5204076,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1562695765100474,"score_gpt":0.4629618888542275,"score_spread":0.3066923123441801,"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."}}