{"id":"W2791915932","doi":"10.1101/279109","title":"Improvement of the memory function of a mutual repression network in a stochastic environment by negative autoregulation","year":2018,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Université de Montréal","keywords":"Bistability; Computer science; Autoregulation; Biological network; Function (biology); Gene regulatory network; Stochastic process; Control theory (sociology); Mathematics; Control (management); Physics; Biology; Artificial intelligence","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000600356,0.0003584823,0.0004416341,0.00008523965,0.0000714409,0.00001417116,0.000385805,0.0004865097,0.00002432636],"category_scores_gemma":[0.00008353445,0.0003204487,0.000223683,0.0002682499,0.0002479065,0.000006056182,0.0007453377,0.0002368632,0.00000212228],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001426176,"about_ca_system_score_gemma":0.0002351516,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003616075,"about_ca_topic_score_gemma":0.000006012213,"domain_scores_codex":[0.9976141,0.0002046656,0.0006823147,0.0007989618,0.0003855269,0.0003144693],"domain_scores_gemma":[0.9973001,0.00001952002,0.0009896266,0.001432763,0.0001795509,0.00007847841],"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":[0.0001326802,0.0001137522,0.005474454,0.00007382566,0.0002288711,2.820037e-7,0.000008798603,0.06805971,0.9252226,0.000006649067,0.000667739,0.00001063657],"study_design_scores_gemma":[0.0006005865,0.0002371521,0.1485813,0.0003230062,0.0002584925,4.477012e-9,0.000005300253,0.005264337,0.8440993,0.000009361641,0.0002408432,0.000380337],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9900354,0.001056026,0.00763831,0.00002501805,0.0004320208,0.000731243,0.0000662745,0.00001308066,0.000002586435],"genre_scores_gemma":[0.9987836,0.00005318289,0.0005589041,0.00002940662,0.0003845707,0.0001189577,0.000002566256,0.00005536608,0.00001341477],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1431068,"threshold_uncertainty_score":0.9999248,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005353256722086243,"score_gpt":0.188273749703449,"score_spread":0.1829204929813627,"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."}}