{"id":"W4416185193","doi":"10.48550/arxiv.2511.08554","title":"A bioreactor-based architecture for in vivo model-based and sim-to-real learning control of microbial consortium composition","year":2025,"lang":"","type":"preprint","venue":"ArXiv.org","topic":"Microbial Metabolic Engineering and Bioproduction","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institute of Genetics; European Commission","keywords":"Synthetic biology; Microbial consortium; Architecture; Software deployment; Scalability; Metabolic engineering","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":[0.0004190839,0.0005562181,0.0007963732,0.0003412919,0.00009998841,0.00002795734,0.0002391969,0.0007497962,0.000005214874],"category_scores_gemma":[0.0001710779,0.0006029464,0.0002966486,0.000187667,0.000180663,0.000003520396,0.0001280213,0.0005506582,6.237075e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005643,"about_ca_system_score_gemma":0.0004408548,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001986194,"about_ca_topic_score_gemma":0.00007160283,"domain_scores_codex":[0.9974139,0.0001638128,0.0007440035,0.001104935,0.0001069883,0.0004663391],"domain_scores_gemma":[0.9986956,0.00004623944,0.0003454723,0.0004939273,0.0002812969,0.0001374371],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001309699,0.0001580251,0.007598347,0.0006944138,0.00008578537,6.445572e-7,0.00006071806,0.3383285,0.6512188,0.000005992161,0.00003197869,0.0005070716],"study_design_scores_gemma":[0.003881088,0.0004710621,0.002473927,0.00055806,0.00023366,0.000002719197,0.00001614159,0.03860686,0.9504688,0.000007059295,0.002706016,0.0005745608],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7941664,0.0003355124,0.2025402,0.0005800272,0.0003985561,0.001246991,0.0006906312,0.00002145616,0.00002024949],"genre_scores_gemma":[0.994764,0.0001336932,0.003629167,0.0002759773,0.0003463106,0.000125522,0.0005481247,0.00004321434,0.0001339855],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2997217,"threshold_uncertainty_score":0.9996422,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009441901205203286,"score_gpt":0.2395906231956877,"score_spread":0.2301487219904844,"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."}}