{"id":"W2137746748","doi":"10.1002/jcc.10267","title":"Peptide models. XXXIII. Extrapolation of low‐level Hartree–Fock data of peptide conformation to large basis set SCF, MP2, DFT, and CCSD(T) results. The Ramachandran surface of alanine dipeptide computed at various levels of theory","year":2003,"lang":"en","type":"article","venue":"Journal of Computational Chemistry","topic":"Advanced Chemical Physics Studies","field":"Physics and Astronomy","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Institutes of Health; National Cancer Institute; Hungarian Scientific Research Fund; U.S. Department of Health and Human Services","keywords":"Ramachandran plot; Ab initio; Dipeptide; Chemistry; Computational chemistry; Extrapolation; Potential energy surface; Statistical physics; Protein structure; Physics; Peptide; Mathematics","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":[0.0005998008,0.0001946233,0.0005175488,0.00003407221,0.00007776221,0.000007435494,0.0003062167,0.00004660362,0.00003621698],"category_scores_gemma":[0.00009794119,0.0001619966,0.0001231022,0.0001872266,0.0001569025,0.0002861824,0.0002112147,0.0001962503,4.316636e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003981959,"about_ca_system_score_gemma":0.0001349522,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000113264,"about_ca_topic_score_gemma":0.000001155443,"domain_scores_codex":[0.9979504,0.00006522272,0.001141794,0.0001766415,0.0005013886,0.0001645391],"domain_scores_gemma":[0.9964424,0.000735156,0.001603508,0.000290947,0.0008477847,0.0000801623],"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":[0.0005020198,0.0003320818,0.002059919,0.0003319577,0.0005646771,7.108353e-7,0.001834206,0.883595,0.1059092,0.003013059,0.001131475,0.0007257758],"study_design_scores_gemma":[0.006776954,0.0001580798,0.008957954,0.0007266462,0.0003060846,0.00002596358,0.001587563,0.08189753,0.6237538,0.2750975,0.0002374557,0.0004744991],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8181913,0.0001158785,0.177214,0.00006065667,0.0000247209,0.0001287734,0.002407253,0.000003580149,0.001853767],"genre_scores_gemma":[0.9880798,0.000007561732,0.01143096,0.00001556051,0.0000558535,9.230533e-7,0.0003168002,0.00001510553,0.00007741697],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8016974,"threshold_uncertainty_score":0.6606029,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03922467383317537,"score_gpt":0.2810829404553448,"score_spread":0.2418582666221694,"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."}}