{"id":"W2656426451","doi":"10.3390/e19070294","title":"An Exploration Algorithm for Stochastic Simulators Driven by Energy Gradients","year":2017,"lang":"en","type":"article","venue":"Entropy","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"U.S. Air Force; Defense Advanced Research Projects Agency; National Science Foundation","keywords":"Metric (unit); Computer science; Sampling (signal processing); Energy landscape; Mahalanobis distance; Algorithm; Degrees of freedom (physics and chemistry); Dimension (graph theory); Set (abstract data type); Phase space; Manifold (fluid mechanics); Energy (signal processing); Space (punctuation); Geometry; Statistical physics; Mathematics; Artificial intelligence; Physics; Computer vision; Filter (signal processing)","routes":{"ca_aff":true,"ca_fund":false,"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":[0.00003020501,0.0001009813,0.00008111534,0.00001288917,0.0002241447,0.00005807722,0.0002185063,0.00009271144,0.00000300089],"category_scores_gemma":[0.0000311677,0.00009609694,0.00004851775,0.000008275108,0.00004305279,0.00001610963,0.00003740558,0.0000257767,0.000001300409],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009615385,"about_ca_system_score_gemma":0.000016554,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001871149,"about_ca_topic_score_gemma":0.00001322774,"domain_scores_codex":[0.9994398,0.000013243,0.0000877879,0.0002294402,0.00006760329,0.0001620628],"domain_scores_gemma":[0.9993802,0.000003156164,0.00008847916,0.0004107604,0.00004450907,0.00007287254],"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":[0.0001809502,0.0001726592,0.0006561635,0.00001091203,0.0001505215,0.00000295587,0.0001000422,0.004376879,0.7904384,0.006559137,0.00362927,0.1937221],"study_design_scores_gemma":[0.004919431,0.002325831,0.0007045047,0.00001961229,0.00009894084,0.000009021863,0.00008497766,0.5561963,0.3677587,0.01937733,0.0474517,0.001053614],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1153245,0.00008284128,0.8840505,0.00004102685,0.000253372,0.0001321068,0.00008217282,0.000009870813,0.00002356428],"genre_scores_gemma":[0.9932343,0.00001269004,0.005418474,0.00008967097,0.0003587277,0.00004194628,0.0006232255,0.00001837292,0.0002026166],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8786321,"threshold_uncertainty_score":0.391872,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008472138541465335,"score_gpt":0.2635665783519234,"score_spread":0.2550944398104581,"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."}}