{"id":"W1973255639","doi":"10.1089/cmb.2012.0089","title":"Determining Protein Structures from NOESY Distance Constraints by Semidefinite Programming","year":2012,"lang":"en","type":"article","venue":"Journal of Computational Biology","topic":"Peroxisome Proliferator-Activated Receptors","field":"Biochemistry, Genetics and Molecular Biology","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; University of Waterloo","funders":"","keywords":"Semidefinite programming; Mathematical optimization; Euclidean distance; Computer science; Algorithm; Euclidean geometry; Simulated annealing; Mathematics; Artificial intelligence","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":[0.0001974374,0.0001496066,0.0002093353,0.00005324111,0.00006424153,0.0000207986,0.000167278,0.0001727905,0.00004321265],"category_scores_gemma":[0.000163793,0.0001246931,0.00008683882,0.00006118671,0.0001953845,0.00001544128,0.00004713378,0.0001914137,0.0000036017],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002560896,"about_ca_system_score_gemma":0.0001087201,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000180484,"about_ca_topic_score_gemma":5.154899e-7,"domain_scores_codex":[0.998937,0.0001313542,0.0003990562,0.0001476912,0.0001261446,0.0002588267],"domain_scores_gemma":[0.9991232,0.00006290959,0.0004297332,0.00007681658,0.0001725463,0.0001348577],"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":[0.0001184097,0.00007361324,0.0452836,0.00000659438,0.000179706,0.000002141981,0.00007738057,0.0002163922,0.9313174,0.0002738795,0.0005453528,0.0219055],"study_design_scores_gemma":[0.001900108,0.0008508323,0.007613617,0.00007186932,0.00004369608,0.0003114188,0.0001631439,0.0000955459,0.9105678,0.004984905,0.07286476,0.0005322895],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9590855,0.001375905,0.03892154,0.00008657482,0.0003026739,0.00009613144,0.00007609636,0.000006159584,0.00004936859],"genre_scores_gemma":[0.9733735,0.00001340718,0.02567079,0.0001793474,0.0005227963,0.000005660438,0.0002093007,0.00001456137,0.00001063019],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0723194,"threshold_uncertainty_score":0.5084839,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00952262510956205,"score_gpt":0.2651261782244295,"score_spread":0.2556035531148675,"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."}}