{"id":"W2060905099","doi":"10.1007/s10858-006-9083-0","title":"A suite of Mathematica notebooks for the analysis of protein main chain 15N NMR relaxation data","year":2006,"lang":"en","type":"article","venue":"Journal of Biomolecular NMR","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":72,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Canadian Institutes of Health Research; Alberta Heritage Foundation for Medical Research; Innovative Research Group Project of the National Natural Science Foundation of China","keywords":"Chemistry; Relaxation (psychology); Nonlinear system; Graphics; Principal component analysis; Visualization; Tensor (intrinsic definition); Principal axis theorem; Anisotropy; Computational science; Computer science; Physics; Data mining; Geometry; Computer graphics (images); Mathematics; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002375053,0.002981289,0.002133138,0.002384311,0.001440227,0.002779171,0.005780482,0.001649721,0.1192395],"category_scores_gemma":[0.01134339,0.002160199,0.002169763,0.003166787,0.0005161726,0.002440709,0.002023305,0.005027534,0.06468467],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009639532,"about_ca_system_score_gemma":0.002073127,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003982923,"about_ca_topic_score_gemma":0.006711193,"domain_scores_codex":[0.9987893,0.0002624118,0.0001528442,0.0001554079,0.0005374888,0.0001026556],"domain_scores_gemma":[0.9954674,0.002215897,0.000165786,0.0008235735,0.001123878,0.0002034081],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003626553,0.0004115186,0.001288902,0.001368084,0.0003956558,0.0006252635,0.0003434208,0.03929615,0.01265136,0.04188873,0.5591556,0.3422126],"study_design_scores_gemma":[0.0006024597,0.0001017693,0.00145342,0.0001961314,0.0001416688,0.0007901663,0.0001045561,0.403427,0.03676451,0.06113494,0.4950209,0.0002624534],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.001632547,0.0002848078,0.8204272,0.0002383553,0.0002838809,0.0001844361,0.01602477,0.1528197,0.008104343],"genre_scores_gemma":[0.01431087,0.0008272031,0.8800278,0.0003052302,0.000160346,0.001172124,0.02058668,0.06694597,0.01566368],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.1192395,"threshold_uncertainty_score":0.398896,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01121028616016207,"score_gpt":0.2636055198652673,"score_spread":0.2523952337051052,"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."}}