{"id":"W4362657028","doi":"10.1371/journal.pone.0282122","title":"Computational capabilities of a multicellular reservoir computing system","year":2023,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Neural Networks and Reservoir Computing","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canada's Michael Smith Genome Sciences Centre; Western Canada Research Grid; Compute Canada","keywords":"Multicellular organism; Reservoir computing; Computer science; Benchmark (surveying); Binary number; Signal processing; Process (computing); Distributed computing; Artificial intelligence; Theoretical computer science; Biological system; Artificial neural network; Biology; Recurrent neural network; Mathematics; Cell; Computer hardware","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":[0.0005372868,0.0001340693,0.0003169356,0.0001677671,0.0002000933,0.0000816451,0.0008031018,0.00005305417,0.000002846297],"category_scores_gemma":[0.00007656082,0.0001242444,0.00008214646,0.00090373,0.00006632599,0.0001717249,0.000666997,0.0001603187,0.00006646802],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004027344,"about_ca_system_score_gemma":0.00004508651,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004244124,"about_ca_topic_score_gemma":0.000001816875,"domain_scores_codex":[0.9980521,0.0001376202,0.0004327072,0.0003555808,0.0006575527,0.00036445],"domain_scores_gemma":[0.9984058,0.0006633796,0.0001668206,0.0004352064,0.0002416723,0.00008710621],"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":[0.00001801462,0.000809273,0.006288094,0.00293349,0.0003626483,0.0001668076,0.003419787,0.9200196,0.007711779,0.05600682,0.0006429138,0.001620767],"study_design_scores_gemma":[0.0002310127,0.00005518053,0.001338614,0.0005652399,0.00001106809,0.000004567021,0.0001924435,0.9943693,0.002297913,0.000789809,0.00001189257,0.0001330171],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9508466,0.000119765,0.04694999,0.0005410159,0.0001657418,0.0002407593,0.000003182314,0.000667668,0.0004653317],"genre_scores_gemma":[0.9431324,0.000003295027,0.05651874,0.00002829371,0.0001397372,0.000004807223,0.000007391693,0.00001315621,0.000152172],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07434964,"threshold_uncertainty_score":0.5066541,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05178942808556741,"score_gpt":0.2357218973973901,"score_spread":0.1839324693118227,"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."}}