{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001408957,0.00007448845,0.0001167733,0.00002175596,0.00001804865,0.000006477807,0.00008530597,0.00008788911,0.000005242192],"category_scores_gemma":[0.0001011243,0.00007255098,0.00007445907,0.00007837923,0.00002770612,0.000006756854,0.00002119582,0.00009275035,1.839232e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006076808,"about_ca_system_score_gemma":0.000170405,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002178547,"about_ca_topic_score_gemma":6.147832e-7,"domain_scores_codex":[0.9992639,0.00001220735,0.0003610469,0.00008371192,0.0002003184,0.00007884458],"domain_scores_gemma":[0.9993715,0.0000128671,0.0003145828,0.00006200504,0.000202557,0.00003645165],"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":[0.00007831194,0.00004115772,0.0002385949,0.00002856299,0.00001373861,0.000003282256,0.00001036884,0.6798264,0.3193316,0.00002602315,0.000003556142,0.0003984914],"study_design_scores_gemma":[0.002461586,0.0002148232,0.004607037,0.00019392,0.00001497777,0.00005565456,0.00003683174,0.005862693,0.9598064,0.02626775,0.0003167102,0.0001615965],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9023456,0.0001769817,0.09725989,0.00006734309,0.00002428575,0.00006178492,0.00001605526,0.000001405009,0.00004666901],"genre_scores_gemma":[0.9964974,0.000004323811,0.003210062,0.00001703241,0.0001229813,0.000001567605,0.0001327011,0.000005629137,0.000008310076],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6739637,"threshold_uncertainty_score":0.2958543,"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."}}