{"id":"W1557782277","doi":"10.1002/cpa.21592","title":"Small‐Noise Analysis and Symmetrization of Implicit Monte Carlo Samplers","year":2015,"lang":"en","type":"preprint","venue":"Communications on Pure and Applied Mathematics","topic":"Meteorological Phenomena and Simulations","field":"Earth and Planetary Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Lawrence Berkeley National Laboratory; Advanced Scientific Computing Research; York University; U.S. Department of Energy; Office of Science; National Science Foundation","keywords":"Symmetrization; Monte Carlo method; Laplace transform; Bayesian probability; Jackknife resampling; Noise (video); Sampling (signal processing); Applied mathematics; Mathematics; Algorithm; Statistical physics; Computer science; Statistics; Mathematical analysis; Estimator; Physics; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005859566,0.0006105016,0.0006826652,0.0009053856,0.0005244263,0.001172935,0.001537148,0.001010889,0.002363604],"category_scores_gemma":[0.03719806,0.0004390544,0.0006540081,0.0005270636,0.002404506,0.002049872,0.001834213,0.00164941,0.0003480565],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001234724,"about_ca_system_score_gemma":0.00154791,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002266695,"about_ca_topic_score_gemma":0.00180354,"domain_scores_codex":[0.998027,0.001041474,0.00009602206,0.0001613617,0.0005435845,0.0001305102],"domain_scores_gemma":[0.9767189,0.01658333,0.001726492,0.002427492,0.002095205,0.0004485741],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003544406,0.00009232347,0.003067049,0.00007695868,0.00004446311,0.0001428121,0.0002338185,0.6435477,0.009266339,0.3098763,0.0007901293,0.03250761],"study_design_scores_gemma":[0.00001320535,0.00001144406,0.000084994,0.000003119795,0.00000218934,0.00001073925,0.000003124622,0.9807674,0.001078258,0.0178913,0.0001294107,0.000004832042],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04676068,0.00007069393,0.951753,0.0001582827,0.00002073919,0.00003666878,0.00002638137,0.0001644739,0.001009036],"genre_scores_gemma":[0.7547034,0.0001319037,0.2422805,0.0001305942,0.00006258082,0.0001381572,0.0001274869,0.000197448,0.002227881],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005859566,"threshold_uncertainty_score":0.03098875,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08933552320742702,"score_gpt":0.2734277627878759,"score_spread":0.1840922395804489,"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."}}