{"id":"W2794575911","doi":"10.1101/291989","title":"Using a System’s Equilibrium Behavior to Reduce Its Energy Dissipation in Non-Equilibrium Processes","year":2018,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced Thermodynamics and Statistical Mechanics","field":"Physics and Astronomy","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Simon Fraser University; University of Alberta; U.S. Department of Energy; Howard Hughes Medical Institute","keywords":"Dissipation; Optical tweezers; Efficient energy use; Autocatalytic reaction; Energy (signal processing); Square root; Molecular machine; General equilibrium theory; Dual (grammatical number); Computer science; Physics; Statistical physics; Thermodynamics; Materials science; Nanotechnology; Economics; Mathematics; Engineering; Optics; 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":[0.0005833126,0.0003935698,0.0004730769,0.0003534177,0.0007634079,0.0007878292,0.0008406382,0.0006416233,0.001888156],"category_scores_gemma":[0.001853456,0.0003072137,0.0002498245,0.0002828573,0.00120681,0.001824699,0.0008267353,0.000942779,0.0004554229],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007192159,"about_ca_system_score_gemma":0.0004562748,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005276263,"about_ca_topic_score_gemma":0.000611888,"domain_scores_codex":[0.9996829,0.00005042228,0.00001930992,0.00008518865,0.000100462,0.00006174086],"domain_scores_gemma":[0.9993662,0.0002652473,0.0001334652,0.0001365587,0.00005398085,0.00004461299],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001867152,0.0002573702,0.004265959,0.000362659,0.00006561198,0.00028572,0.000412949,0.05428093,0.8655135,0.04767999,0.000422954,0.02626558],"study_design_scores_gemma":[0.00004892851,0.0007225676,0.009139207,0.00005168147,0.00004673678,0.0002922888,0.0002783775,0.3933634,0.5401729,0.0485693,0.007219892,0.00009466596],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7853066,0.00134531,0.2074856,0.0006793342,0.00009485152,0.0001157804,0.0001357197,0.0005682181,0.004268473],"genre_scores_gemma":[0.9791074,0.0003869424,0.01943607,0.00008233877,0.00001347236,0.00006372012,0.00006388659,0.00007088048,0.0007752788],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001888156,"threshold_uncertainty_score":0.006316483,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01962002819551454,"score_gpt":0.2680300060505528,"score_spread":0.2484099778550382,"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."}}