{"id":"W2760631172","doi":"10.1016/j.spa.2018.09.009","title":"Large deviations of Markov chains with multiple time-scales","year":2018,"lang":"en","type":"article","venue":"Stochastic Processes and their Applications","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"Statistics Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Markov chain; Large deviations theory; Statistical physics; Limit (mathematics); Central limit theorem; Mathematics; Markov process; Applied mathematics; Scale (ratio); Standard deviation; Path (computing); Computer science; Statistics; Physics; Mathematical analysis","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":[],"consensus_categories":[],"category_scores_codex":[0.00006426426,0.0001105061,0.0001214281,0.00003639158,0.0001701096,0.000009513103,0.0001125287,0.00005174267,0.0000116317],"category_scores_gemma":[0.00002806408,0.00008399985,0.00002794141,0.0002494727,0.000179635,0.000003040933,0.00005801038,0.00002625115,0.000005420209],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003398853,"about_ca_system_score_gemma":0.00007005119,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002443465,"about_ca_topic_score_gemma":0.000178036,"domain_scores_codex":[0.9994255,0.000009397366,0.0001271248,0.000237313,0.00005593729,0.0001447024],"domain_scores_gemma":[0.9992967,0.00002512951,0.00009052954,0.0002665148,0.0002633007,0.00005784841],"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":[0.000692171,0.002314215,0.01013058,0.001208121,0.002845105,5.168095e-7,0.003117784,0.001228329,0.9174406,0.03000096,0.003537312,0.0274843],"study_design_scores_gemma":[0.01291479,0.006632851,0.02450232,0.0007453523,0.002331354,0.0002473904,0.009823026,0.1243061,0.6509328,0.03435824,0.1267161,0.006489723],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09227447,0.001000455,0.906054,0.00006304647,0.000004317665,0.0002447631,0.00007831985,0.00001311604,0.0002674652],"genre_scores_gemma":[0.9985304,0.00002505927,0.0007340541,0.00003398715,0.0001482891,0.0001726185,0.0001266939,0.00001513278,0.000213715],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.906256,"threshold_uncertainty_score":0.3425414,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004140994575675266,"score_gpt":0.2129286053767068,"score_spread":0.2087876108010315,"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."}}