{"id":"W2137504100","doi":"10.1142/s0219720011005276","title":"ERROR TOLERANT NMR BACKBONE RESONANCE ASSIGNMENT AND AUTOMATED STRUCTURE GENERATION","year":2010,"lang":"en","type":"article","venue":"Journal of Bioinformatics and Computational Biology","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; University of Waterloo","funders":"","keywords":"Probabilistic logic; Computer science; Precision and recall; Protein secondary structure; Algorithm; Artificial intelligence; Biology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002829107,0.001407464,0.001124994,0.001301838,0.0007609432,0.001294529,0.002224685,0.0009838411,0.004525076],"category_scores_gemma":[0.007416327,0.0008802733,0.0008250315,0.001409384,0.000647373,0.001728031,0.001507343,0.001513153,0.002137573],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007442161,"about_ca_system_score_gemma":0.001546336,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001827654,"about_ca_topic_score_gemma":0.001853486,"domain_scores_codex":[0.9976974,0.0007272478,0.0001307746,0.0005813958,0.000724139,0.0001388606],"domain_scores_gemma":[0.9966533,0.001287003,0.0004242005,0.0007178654,0.0008406956,0.00007705615],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00104308,0.0002546812,0.004019055,0.0005650343,0.0001652402,0.0005439487,0.000403174,0.2553284,0.1742963,0.01655991,0.01539983,0.5314215],"study_design_scores_gemma":[0.00005179273,0.0001133216,0.0006999685,0.00001714613,0.00002268149,0.0001691886,0.00005830537,0.9040855,0.07948809,0.008686465,0.0065606,0.00004700256],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0201672,0.00006194744,0.9659593,0.0001017743,0.00002697931,0.00007864599,0.0003503545,0.01254066,0.0007129951],"genre_scores_gemma":[0.1041095,0.00008642307,0.891758,0.0000760499,0.00001970771,0.000201496,0.001485616,0.001236687,0.001026566],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004525076,"threshold_uncertainty_score":0.01513785,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006003271850188033,"score_gpt":0.2457398115828957,"score_spread":0.2397365397327077,"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."}}