{"id":"W4402427239","doi":"10.48550/arxiv.2408.06894","title":"Exploring the generalizability of the optimal 0.234 acceptance rate in random-walk Metropolis and parallel tempering algorithms","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"DNA and Biological Computing","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Government of Canada; Canadian Institute for Advanced Research","keywords":"Generalizability theory; Random walk; Parallel tempering; Computer science; Algorithm; Mathematics; Artificial intelligence; Statistics; Markov chain Monte Carlo","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":[],"consensus_categories":[],"category_scores_codex":[0.0004999198,0.0002214507,0.0002615281,0.00004227466,0.00009755173,0.00003417887,0.0005620332,0.0001602258,0.000004835586],"category_scores_gemma":[0.00008569002,0.0001398707,0.0002081936,0.0002244743,0.0002672769,0.000005642185,0.002521017,0.0004096089,9.41816e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003833517,"about_ca_system_score_gemma":0.00006172237,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001949449,"about_ca_topic_score_gemma":0.0000501949,"domain_scores_codex":[0.9985687,0.0002902051,0.0002090773,0.0006561593,0.00004349842,0.0002324189],"domain_scores_gemma":[0.9992215,0.00005397845,0.0001161635,0.0005153213,0.00004878418,0.00004425123],"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":[0.001139472,0.0002480511,0.09574196,0.0007972897,0.0007882114,0.0001320034,0.0007288422,0.7605668,0.1247031,0.008448527,0.0001902259,0.006515488],"study_design_scores_gemma":[0.01202406,0.001032495,0.2881866,0.001467948,0.001176851,0.00004365318,0.006181335,0.4376438,0.206244,0.03251633,0.008614154,0.004868777],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9958547,0.0006768331,0.002452046,0.00008791537,0.0003257607,0.000276311,0.00001671625,0.00001413105,0.0002955734],"genre_scores_gemma":[0.9984022,0.0009799857,0.0002076487,0.00004733978,0.0001205143,0.000003636172,0.000008568137,0.00001226831,0.0002178755],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.322923,"threshold_uncertainty_score":0.5703763,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1072132875300583,"score_gpt":0.2110754600681528,"score_spread":0.1038621725380944,"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."}}