{"id":"W4386389416","doi":"10.2139/ssrn.4559765","title":"A Reinforcement Learning Approach to Find Optimal Propulsion Strategy for Microrobots Swimming at Low Reynolds Number","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Micro and Nano Robotics","field":"Physics and Astronomy","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Reynolds number; Propulsion; Reinforcement learning; Reinforcement; Aerospace engineering; Computer science; Aeronautics; Mechanics; Physics; Artificial intelligence; Engineering; Structural engineering","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.001317584,0.0005578081,0.0005956077,0.0001737802,0.000639294,0.0002489007,0.000578178,0.0002722151,0.00007986125],"category_scores_gemma":[0.00001621423,0.0005305831,0.0005090059,0.0001790491,0.00002816196,0.00009791504,0.0006927409,0.003918201,0.000168548],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0016776,"about_ca_system_score_gemma":0.002377684,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001662528,"about_ca_topic_score_gemma":0.00002457595,"domain_scores_codex":[0.9949276,0.00008888016,0.0007594599,0.0007083461,0.0003591961,0.003156512],"domain_scores_gemma":[0.9985924,0.00004561571,0.0005866013,0.0003620319,0.0002006706,0.000212709],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002430343,0.0001695608,0.001252593,0.0001582067,0.000716036,0.000001810098,0.0006539137,0.9693877,0.003866153,0.0116785,0.001230816,0.01064166],"study_design_scores_gemma":[0.03592144,0.01031254,0.002786692,0.008625444,0.005283528,0.001485666,0.04026632,0.3438059,0.05372556,0.4296725,0.04542711,0.0226873],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2946239,0.0002779787,0.7011012,0.0001928646,0.0006137117,0.001594423,0.000023972,0.00008008355,0.001491838],"genre_scores_gemma":[0.9298566,0.0002224184,0.004815073,0.00002078352,0.001535368,0.0001648748,0.0004868341,0.0001664698,0.06273156],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6962861,"threshold_uncertainty_score":0.9997146,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02273184429979114,"score_gpt":0.2773772555231605,"score_spread":0.2546454112233694,"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."}}