{"id":"W2993023536","doi":"10.1109/cdc40024.2019.9029988","title":"Coupling and synchronization of piecewise linear genetic regulatory systems","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Agence Nationale de la Recherche","keywords":"Synchronization (alternating current); Formalism (music); Piecewise linear function; Coupling (piping); Nonlinear system; Dynamical systems theory; Nonlinear dynamical systems; Differential equation; Control theory (sociology); Topology (electrical circuits); Computer science; Mathematics; Statistical physics; Biological system; Physics; Mathematical analysis; Quantum mechanics; Biology; Artificial intelligence; Engineering; Combinatorics","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"],"consensus_categories":[],"category_scores_codex":[0.0002125919,0.0002666228,0.0004260862,0.00009305987,0.00003311192,0.00002449218,0.0002120651,0.0005383824,0.00001293413],"category_scores_gemma":[0.00002146368,0.0002680415,0.0001385602,0.00007539966,0.00008771888,0.000001264392,0.0005812697,0.0001263614,0.000004703517],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002538829,"about_ca_system_score_gemma":0.00018752,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004192045,"about_ca_topic_score_gemma":0.00001003468,"domain_scores_codex":[0.9984956,0.00004600312,0.0004492986,0.0006152103,0.0002032573,0.0001905701],"domain_scores_gemma":[0.998404,0.000009134989,0.0003411568,0.0009380322,0.0002294163,0.00007831678],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001403882,0.00002203614,0.01866158,0.0006038545,0.000329769,7.923159e-7,0.00001269319,0.9493476,0.030202,0.00003724169,0.0004710471,0.0002973693],"study_design_scores_gemma":[0.0004719106,0.0001372091,0.01169919,0.0002613057,0.0004725062,0.00001542004,0.00007268617,0.9618484,0.02278524,0.00003956705,0.001528817,0.0006677122],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9441118,0.02147609,0.03341508,0.00001036274,0.0003491581,0.0003862823,0.00001601762,0.00001612045,0.0002191358],"genre_scores_gemma":[0.9951242,0.001555482,0.00142233,0.00001185236,0.0003885544,0.00001592247,0.0002699085,0.00005065939,0.001161129],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05101241,"threshold_uncertainty_score":0.9999772,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007575576519835042,"score_gpt":0.2224876117204402,"score_spread":0.2149120352006051,"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."}}