{"id":"W2923404455","doi":"10.1073/pnas.1817778116","title":"Using a system’s equilibrium behavior to reduce its energy dissipation in nonequilibrium processes","year":2019,"lang":"en","type":"article","venue":"Proceedings of the National Academy of Sciences","topic":"Advanced Thermodynamics and Statistical Mechanics","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Simon Fraser University; Howard Hughes Medical Institute; Canada Research Chairs; Government of Canada; University of California Institute for Mexico and the United States; U.S. Department of Energy","keywords":"Dissipation; Autocatalytic reaction; Optical tweezers; Efficient energy use; Energy (signal processing); Square root; Physics; Computer science; Statistical physics; Thermodynamics; Mathematics; Engineering; 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":[0.0002913672,0.00008660178,0.0001522593,0.0001122908,0.00004731479,0.00002080661,0.0004030667,0.0000329982,0.00001284858],"category_scores_gemma":[0.00007029306,0.00006445096,0.00003293532,0.0007625098,0.00006635092,0.0003624585,0.0001186072,0.00007267642,0.000001077199],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004355341,"about_ca_system_score_gemma":0.00005376953,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001564955,"about_ca_topic_score_gemma":6.176971e-8,"domain_scores_codex":[0.9987992,0.000004390719,0.0002887707,0.0002211217,0.0005326905,0.000153796],"domain_scores_gemma":[0.9993861,0.00005586798,0.0002653088,0.000008333542,0.0002485193,0.00003583942],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001114169,0.00004055506,0.002964148,0.00008385546,0.000003202786,2.623965e-9,0.00005400995,0.001589571,0.4886129,0.5063655,0.000002758706,0.0002723598],"study_design_scores_gemma":[0.0004392387,0.0001183231,0.01534905,0.0007870885,0.00002953751,0.000003086009,0.0007934906,0.3139142,0.5613719,0.1068175,0.00004253622,0.0003341214],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9955964,0.00001702042,0.0002966125,0.0001486904,0.0000303333,0.0002350986,0.00005249951,0.000005775733,0.003617536],"genre_scores_gemma":[0.9975277,3.984472e-7,0.002292034,0.00001688128,0.00004555601,0.00002273617,3.29178e-7,0.000005745425,0.00008865863],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3995481,"threshold_uncertainty_score":0.2628234,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04197845031772632,"score_gpt":0.3302509564244493,"score_spread":0.2882725061067231,"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."}}