{"id":"W4414554071","doi":"10.1038/s41598-025-18261-x","title":"Input driven optimization of echo state network parameters for prediction on chaotic time series","year":2025,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Neural Networks and Reservoir Computing","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Reservoir computing; Hyperparameter; Echo state network; Series (stratigraphy); Chaotic; Time series; Network topology; Echo (communications protocol)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001071045,0.0007590606,0.0005415288,0.0003595268,0.0002526474,0.0005731867,0.0007010735,0.0008340012,0.0007861314],"category_scores_gemma":[0.005810377,0.0003962838,0.0003179535,0.0003226742,0.0006278914,0.001455925,0.0007815526,0.001204535,0.0001512953],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005396602,"about_ca_system_score_gemma":0.000776297,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002499656,"about_ca_topic_score_gemma":0.003351014,"domain_scores_codex":[0.9997993,0.00007393558,0.00001372309,0.00005473946,0.00003526413,0.00002307972],"domain_scores_gemma":[0.9983407,0.001148854,0.0001742464,0.00008854901,0.000200787,0.00004688508],"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":[0.00001806506,0.00001609213,0.0006109123,0.00001846055,0.00001288576,0.00001653018,0.00001697515,0.9874329,0.001126466,0.001318486,0.0001754567,0.009236829],"study_design_scores_gemma":[7.882089e-7,0.000002448418,0.00002953521,9.365481e-7,7.241197e-7,0.000001301606,0.000001186748,0.9992093,0.0002477972,0.0004865328,0.00001852669,9.441781e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1176666,0.0001923769,0.8800597,0.0002648494,0.00002461931,0.00004118687,0.00009901695,0.0004315019,0.001220018],"genre_scores_gemma":[0.9244435,0.0001014095,0.07431428,0.00006106055,0.00001410242,0.00009320653,0.0001424199,0.00006755431,0.0007624179],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002499656,"threshold_uncertainty_score":0.005664349,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009385920064121995,"score_gpt":0.2236857365089987,"score_spread":0.2142998164448767,"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."}}