{"id":"W2015236698","doi":"10.1021/jp309420u","title":"Extracting Conformational Memory from Single-Molecule Kinetic Data","year":2012,"lang":"en","type":"article","venue":"The Journal of Physical Chemistry B","topic":"Advanced Fluorescence Microscopy Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of General Medical Sciences; National Institutes of Health; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Computer science; Markov chain; Markov model; Markov process; Algorithm; Statistical physics; Maximum-entropy Markov model; Theoretical computer science; Kernel (algebra); Set (abstract data type); Hidden Markov model; Variable-order Markov model; Artificial intelligence; Mathematics; Discrete mathematics; Machine learning; Physics; Statistics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001256926,0.0005219516,0.0007583878,0.001409699,0.0002715391,0.0009387505,0.0008996933,0.0007928315,0.00101761],"category_scores_gemma":[0.006158847,0.0005243789,0.0005925202,0.001186644,0.0005158075,0.002209282,0.0006209104,0.001113703,0.0005557616],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005113473,"about_ca_system_score_gemma":0.0007437397,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001019767,"about_ca_topic_score_gemma":0.0009721706,"domain_scores_codex":[0.9997787,0.00003438745,0.00002491588,0.00006588166,0.00006554013,0.0000305785],"domain_scores_gemma":[0.997981,0.001027125,0.0002516205,0.0004944433,0.0001802504,0.00006557976],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008631778,0.000332692,0.01431113,0.001383816,0.0002820154,0.000853248,0.0005059612,0.3682297,0.2924869,0.0507998,0.003463542,0.2664881],"study_design_scores_gemma":[0.00001844892,0.00006508824,0.003896173,0.0000379744,0.00003616169,0.0001851175,0.00008049046,0.9021059,0.06183455,0.02936965,0.002286951,0.00008343798],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2221471,0.0006004044,0.7720673,0.0002986734,0.00005710843,0.00006098114,0.001321133,0.002025233,0.00142202],"genre_scores_gemma":[0.7593508,0.0009312526,0.2360455,0.00008769787,0.00003681816,0.0000893799,0.002279835,0.0003137561,0.0008648461],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001409699,"threshold_uncertainty_score":0.006647348,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02396627081335522,"score_gpt":0.3033640286331513,"score_spread":0.279397757819796,"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."}}