{"id":"W4296704968","doi":"10.1103/physrevlett.129.130601","title":"Bayesian Information Engine that Optimally Exploits Noisy Measurements","year":2022,"lang":"en","type":"article","venue":"Physical Review Letters","topic":"Advanced Thermodynamics and Statistical Mechanics","field":"Physics and Astronomy","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Foundational Questions Institute; Silicon Valley Community Foundation","keywords":"Noise (video); SIGNAL (programming language); Bayesian probability; Energy (signal processing); Bead; Gravitation; Position (finance); Signal-to-noise ratio (imaging); Thermal; Physics; Acoustics; Computer science; Materials science; Optics; Classical mechanics; Artificial intelligence; Thermodynamics; Quantum mechanics","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":[0.0001350659,0.0001604605,0.0002579057,0.00002443501,0.0001583719,0.00002332979,0.0002075169,0.000002653721,0.0004058337],"category_scores_gemma":[0.000009406895,0.0001512204,0.0001309685,0.0001516288,0.00001396038,0.0002621412,0.0001126928,0.0002194561,0.00005803248],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005935878,"about_ca_system_score_gemma":0.00001516492,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006863149,"about_ca_topic_score_gemma":7.46327e-8,"domain_scores_codex":[0.9989216,0.00006519933,0.0002027772,0.0001560398,0.0004099766,0.0002444386],"domain_scores_gemma":[0.9995066,0.00004330872,0.0001241015,0.0002091525,0.00002806588,0.00008874937],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007596449,0.001026881,0.000713953,0.001217552,0.0004683583,0.00001099094,0.0007534901,0.04497811,0.01433748,0.3393039,0.008280236,0.5888332],"study_design_scores_gemma":[0.00334954,0.0005918127,0.0008259632,0.001342511,0.0006972681,0.000007510809,0.0003714158,0.694219,0.00151291,0.08914563,0.2047671,0.003169372],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01592237,0.000344773,0.9775586,0.002816813,0.0002223652,0.0006503792,0.0001884322,0.00005507002,0.002241273],"genre_scores_gemma":[0.9909828,0.00003951724,0.002272984,0.006162452,0.00009524132,0.0002375518,0.0001838293,0.00001814891,0.000007428969],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9752855,"threshold_uncertainty_score":0.6166589,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02009188679548353,"score_gpt":0.2693323153259917,"score_spread":0.2492404285305082,"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."}}