{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002542579,0.001769112,0.001651329,0.0006343941,0.0005467367,0.001449896,0.001700789,0.001464581,0.004133719],"category_scores_gemma":[0.006142571,0.001142164,0.001150823,0.000795509,0.001951726,0.001845912,0.00181342,0.00327888,0.000810551],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001060034,"about_ca_system_score_gemma":0.001869644,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002961699,"about_ca_topic_score_gemma":0.003795673,"domain_scores_codex":[0.9984674,0.0007362688,0.00005957198,0.0002452522,0.0004027889,0.00008877079],"domain_scores_gemma":[0.9944929,0.004284854,0.0003657578,0.0002760146,0.000458305,0.0001221544],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003874556,0.00006065281,0.0001575432,0.000122822,0.00002220682,0.00007459975,0.00003748825,0.953386,0.0009628433,0.02963807,0.001590201,0.01390885],"study_design_scores_gemma":[0.00001785327,0.00002139995,0.00003203121,0.000007943032,0.000002793326,0.00001255538,0.00001385205,0.9749747,0.0004048463,0.02358682,0.0009185083,0.000006764883],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006171725,0.0001128242,0.9898163,0.0002982793,0.00003568143,0.00006874706,0.0001506284,0.000138347,0.003207445],"genre_scores_gemma":[0.1690652,0.0004939943,0.8213192,0.0004032774,0.0001132924,0.0008922951,0.001056715,0.0003763471,0.006279598],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004133719,"threshold_uncertainty_score":0.01382869,"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."}}