{"id":"W3015713838","doi":"10.1371/journal.pcbi.1007783","title":"Deep reinforcement learning for the control of microbial co-cultures in bioreactors","year":2020,"lang":"en","type":"article","venue":"PLoS Computational Biology","topic":"Innovative Microfluidic and Catalytic Techniques Innovation","field":"Engineering","cited_by":127,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"H2020 European Research Council; European Commission; Natural Sciences and Engineering Research Council of Canada; Wellcome Trust","keywords":"Bioreactor; Reinforcement learning; Reinforcement; Biology; Biochemical engineering; Microbiology; Artificial intelligence; Computer science; Engineering","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":[0.00008040106,0.00007391331,0.0001235174,0.00004782857,0.00003005376,0.0000039344,0.00008859463,0.00005425305,0.0000171658],"category_scores_gemma":[0.00006554822,0.00005592945,0.00002714327,0.0001577183,0.0000667893,0.00002230375,0.000009991651,0.0001079843,0.000002680409],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002982854,"about_ca_system_score_gemma":0.00001347857,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002582472,"about_ca_topic_score_gemma":1.552977e-7,"domain_scores_codex":[0.9995161,0.00001387259,0.0002518169,0.00008122159,0.00003998669,0.00009697203],"domain_scores_gemma":[0.9996244,0.0001805064,0.00005368989,0.00003067327,0.0001016547,0.00000911057],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006081797,0.00001355935,0.001520522,0.00005214558,0.0001042636,2.616783e-7,0.0005894691,0.3526493,0.6134627,0.02838775,0.001007439,0.002151699],"study_design_scores_gemma":[0.000959811,0.0001761509,0.00107927,0.00001214732,0.00001375292,0.000001230526,0.00009378322,0.8318567,0.1574786,0.001030695,0.007167183,0.0001306434],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02431405,0.0001227012,0.9745594,0.0004191016,0.00004134953,0.000300298,0.00001083591,0.00006387033,0.0001683964],"genre_scores_gemma":[0.9982543,0.000004574668,0.0009448385,0.0003890286,0.00005619752,0.0000340276,0.0003069108,0.000008040221,0.000002081255],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9739403,"threshold_uncertainty_score":0.2280737,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01714102203565858,"score_gpt":0.25526875325128,"score_spread":0.2381277312156214,"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."}}