{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004825014,0.001279246,0.00175802,0.001411197,0.0008318297,0.002389138,0.002362355,0.002293647,0.001763839],"category_scores_gemma":[0.01445712,0.0009088668,0.0005532119,0.001022061,0.001665093,0.004746927,0.00241556,0.001761743,0.0008328569],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001657806,"about_ca_system_score_gemma":0.003890228,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003888442,"about_ca_topic_score_gemma":0.004466475,"domain_scores_codex":[0.9973329,0.0006187641,0.0001972643,0.0005979724,0.0008566327,0.0003964874],"domain_scores_gemma":[0.9943746,0.003111461,0.0005670075,0.0007649774,0.0008732843,0.000308677],"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":[0.002026794,0.0006335603,0.004195457,0.0002977345,0.0002623872,0.0003491833,0.000289028,0.6151498,0.06491033,0.1252507,0.003356258,0.1832787],"study_design_scores_gemma":[0.00003915356,0.00008225904,0.0002534359,0.00001165754,0.00002089981,0.00003152261,0.00001094977,0.9660649,0.01206838,0.02083973,0.0005350996,0.0000420385],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1142628,0.0004588908,0.877398,0.0008014342,0.00010812,0.0001318847,0.000258876,0.001846577,0.004733531],"genre_scores_gemma":[0.8339485,0.0001831265,0.1623007,0.0002786795,0.00006233868,0.0001104543,0.0003057572,0.000127048,0.002683389],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004825014,"threshold_uncertainty_score":0.0255174,"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."}}