{"id":"W2044145005","doi":"10.1371/journal.pone.0023294","title":"Generalized Fragment Picking in Rosetta: Design, Protocols and Applications","year":2011,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":209,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children","funders":"National Institute of General Medical Sciences; Howard Hughes Medical Institute","keywords":"Computer science; Modularity (biology); Workflow; Fragment (logic); Extensibility; Programming language; Range (aeronautics); Data structure; Code (set theory); Theoretical computer science; Data mining; Database","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":[0.0000726052,0.00007292817,0.0000900412,0.00002153649,0.00003052302,0.00000868695,0.0000791683,0.00007637165,0.000009256836],"category_scores_gemma":[0.00001183288,0.00006980037,0.0000128166,0.0000406253,0.00002739493,0.000002075139,0.00005578036,0.00004728701,0.000001975306],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006855131,"about_ca_system_score_gemma":0.00002012451,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001419568,"about_ca_topic_score_gemma":0.00001715049,"domain_scores_codex":[0.9995087,0.00002601665,0.0001081572,0.0001814317,0.0000607978,0.0001149384],"domain_scores_gemma":[0.9997121,0.000002566757,0.0000352846,0.000190376,0.00002419914,0.00003544461],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00006523878,0.0003327592,0.01197703,0.00005048696,0.00005320718,0.000001508574,0.00007918175,0.000006470687,0.9851242,0.0007100433,0.00002022559,0.001579692],"study_design_scores_gemma":[0.0007756408,0.0002071833,0.006707933,0.00004384653,0.00002178971,0.000002328851,0.00001033651,0.0001506338,0.9880839,0.002932869,0.0008765766,0.0001869475],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9175441,0.0005203459,0.06549057,0.00005136279,0.000007437573,0.01520445,0.00001240887,0.00002463647,0.00114469],"genre_scores_gemma":[0.8685131,0.00009000589,0.1096629,0.0002845368,0.0001340296,0.02104477,0.00004281818,0.00002250752,0.0002052764],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04903098,"threshold_uncertainty_score":0.2846377,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05278565982265621,"score_gpt":0.2567203854971396,"score_spread":0.2039347256744834,"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."}}