{"id":"W4407690636","doi":"10.1371/journal.pcsy.0000034","title":"Signal demixing using multi-delay multi-layer reservoir computing","year":2025,"lang":"en","type":"article","venue":"PLOS complex systems.","topic":"Neural Networks and Reservoir Computing","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reservoir computing; Computer science; Layer (electronics); SIGNAL (programming language); Application layer; Distributed computing; Real-time computing; Materials science; Artificial intelligence; Operating system; Artificial neural network; Nanotechnology","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008518185,0.0004944055,0.0007755694,0.0003947864,0.001141011,0.0008488984,0.002344558,0.0001955107,0.000009977929],"category_scores_gemma":[0.00007680381,0.0004483986,0.0002286131,0.001368089,0.00008964109,0.0004913507,0.002079189,0.0006160566,0.00003493282],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002347718,"about_ca_system_score_gemma":0.0001620953,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003549899,"about_ca_topic_score_gemma":0.00002509155,"domain_scores_codex":[0.9954515,0.0006083263,0.001107372,0.001106661,0.00061639,0.00110975],"domain_scores_gemma":[0.9973722,0.0005617251,0.0004135466,0.00108186,0.0003346668,0.0002360186],"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":[0.00002705552,0.0006943498,0.0173545,0.001076557,0.0005135045,0.0003523813,0.0009685773,0.9013385,0.05602948,0.01485718,0.002754753,0.004033217],"study_design_scores_gemma":[0.0009344167,0.0000298481,0.001610004,0.001043743,0.00002507931,0.00006174132,0.00009185657,0.993988,0.0004645553,0.00005509368,0.001223874,0.0004717795],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1450467,0.001277396,0.8501832,0.0002378137,0.001314352,0.000683618,0.000003551269,0.0006676287,0.0005857092],"genre_scores_gemma":[0.8394766,0.000004191786,0.1593901,0.0003213517,0.0003561238,0.00001030183,0.000004477369,0.00003577192,0.0004010653],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6944299,"threshold_uncertainty_score":0.9997967,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1434751125590263,"score_gpt":0.3269208481442064,"score_spread":0.1834457355851801,"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."}}