{"id":"W4407638784","doi":"10.1109/fmlds63805.2024.00070","title":"Consensus Control of Micro Multi-Agent Reinforcement Learning Systems for Tumor Treatment","year":2024,"lang":"en","type":"article","venue":"","topic":"Mathematical Biology Tumor Growth","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Concordia University","funders":"","keywords":"Reinforcement learning; Computer science; Multi-agent system; Reinforcement; Control (management); Artificial intelligence; 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":[],"consensus_categories":[],"category_scores_codex":[0.0004717296,0.0002211604,0.0005375034,0.00008539284,0.00005410924,0.00003088896,0.00009800094,0.00006404973,0.000134676],"category_scores_gemma":[0.0004715744,0.0001449069,0.0002097625,0.00006727725,0.00007891294,0.00001542537,0.0000243847,0.00007957677,0.00006562389],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001373861,"about_ca_system_score_gemma":0.00006149499,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003008653,"about_ca_topic_score_gemma":0.000003179347,"domain_scores_codex":[0.9985523,0.0000803717,0.0006768767,0.000263295,0.0001271087,0.0003000941],"domain_scores_gemma":[0.9973426,0.002110734,0.0001410493,0.0002351263,0.00008886421,0.00008159845],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001869199,0.0005561342,0.0003865882,0.006013011,0.001304007,0.00008878166,0.00119798,0.0007365178,0.03500951,0.9498891,0.004262349,0.0003691282],"study_design_scores_gemma":[0.007765738,0.002771631,0.00001986082,0.001015207,0.000996248,0.0002363568,0.002092524,0.8879953,0.06058893,0.01867379,0.01708483,0.0007595829],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1692421,0.002397254,0.8123007,0.0006235871,0.0008976371,0.006104446,0.0001103891,0.0007756413,0.00754819],"genre_scores_gemma":[0.9726704,0.00001142267,0.01411851,0.0000280801,0.00005314367,0.0003473184,0.000008459026,0.0000354719,0.01272718],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9312153,"threshold_uncertainty_score":0.5909129,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06119738620126405,"score_gpt":0.3254913543703904,"score_spread":0.2642939681691263,"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."}}