{"id":"W2107409513","doi":"10.1002/jcc.20109","title":"Biased fragment distribution in MC simulation of protein folding","year":2004,"lang":"en","type":"article","venue":"Journal of Computational Chemistry","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Compute Canada","funders":"Université de Montréal; Centres de Recerca de Catalunya","keywords":"Monte Carlo method; Protein folding; Folding (DSP implementation); Statistical physics; Fragment (logic); Selection (genetic algorithm); Sampling (signal processing); Computer science; Molecular dynamics; Distribution (mathematics); Chemistry; Biological system; Computational chemistry; Algorithm; Physics; Mathematics; Statistics; Biology; Artificial intelligence; Engineering; Biochemistry","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":[0.002076597,0.0004328194,0.0009791533,0.0008540062,0.0008072401,0.0007851705,0.001638425,0.001832093,0.001653094],"category_scores_gemma":[0.009045163,0.0004317385,0.0004219446,0.001133256,0.001423704,0.001059022,0.0009365162,0.0008328398,0.000325996],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001651289,"about_ca_system_score_gemma":0.00105141,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009233139,"about_ca_topic_score_gemma":0.003362932,"domain_scores_codex":[0.9991956,0.000412706,0.00001773082,0.00004775255,0.0002435295,0.00008261437],"domain_scores_gemma":[0.9972875,0.001897519,0.0001549832,0.0001648205,0.0003204461,0.0001747395],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007274874,0.00001900209,0.0007860474,0.00002895111,0.00001590782,0.00008613271,0.00003244301,0.9626052,0.0006468449,0.03278347,0.0003710534,0.002552172],"study_design_scores_gemma":[0.00001405412,0.000006933803,0.00009054599,0.00000367029,0.000002128504,0.000007121015,0.00000212639,0.9962227,0.0001153721,0.003374625,0.0001569854,0.000003746143],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3491369,0.002569099,0.6291695,0.001203438,0.0002339838,0.0001982871,0.0003494634,0.0006337924,0.01650547],"genre_scores_gemma":[0.9364085,0.0007998087,0.05920867,0.0002854358,0.00008074569,0.0003800801,0.0002356994,0.0001712318,0.002429948],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009233139,"threshold_uncertainty_score":0.01835883,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00636399878889167,"score_gpt":0.2534918574883495,"score_spread":0.2471278586994578,"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."}}