{"id":"W2338834611","doi":"10.1016/j.bpj.2015.11.861","title":"Automatic Protein Structure Determination from Sparse NMR Spectroscopy Data","year":2016,"lang":"en","type":"article","venue":"Biophysical Journal","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Discriminative model; Inference; Metric (unit); Computer science; Bayesian inference; Artificial intelligence; Bayesian probability; Machine learning; Data mining; Pattern recognition (psychology)","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":[0.0009726643,0.001037143,0.00144229,0.001580908,0.0005532538,0.001115583,0.001220538,0.0009729675,0.001216751],"category_scores_gemma":[0.003531345,0.0008267754,0.001020689,0.001376524,0.0005930736,0.001442023,0.00150308,0.00155498,0.001196818],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003343764,"about_ca_system_score_gemma":0.001718228,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002496812,"about_ca_topic_score_gemma":0.00592165,"domain_scores_codex":[0.9994171,0.0001488968,0.00003547891,0.0001326131,0.0002147576,0.00005110981],"domain_scores_gemma":[0.9981492,0.0007887448,0.0002206574,0.0004255472,0.0003344131,0.00008160542],"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":[0.0007812587,0.0002556829,0.002177806,0.0005068987,0.0002105756,0.0002817395,0.000165314,0.1192594,0.2293727,0.005411552,0.008507243,0.6330699],"study_design_scores_gemma":[0.00002663827,0.00004750973,0.0007032953,0.000010879,0.00003559713,0.0001519025,0.00002725633,0.9755062,0.01551692,0.00649837,0.001458393,0.00001707239],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04915639,0.0003418858,0.9453604,0.0002217423,0.00003653277,0.00006536495,0.0005329615,0.003670928,0.0006138838],"genre_scores_gemma":[0.2586735,0.0005507882,0.7349715,0.000128483,0.00009019411,0.0001239711,0.00367746,0.0004324699,0.001351512],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002496812,"threshold_uncertainty_score":0.005144,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01022607978634088,"score_gpt":0.2584045068666025,"score_spread":0.2481784270802617,"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."}}