{"id":"W2076132565","doi":"10.1007/s10930-006-9016-5","title":"Quantitative Analysis of the Conservation of the Tertiary Structure of Protein Segments","year":2006,"lang":"en","type":"article","venue":"The Protein Journal","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ontario Institute for Cancer Research; University of Alberta","funders":"","keywords":"Protein Data Bank; Set (abstract data type); Protein Data Bank (RCSB PDB); Sequence (biology); Protein structure; Protein structure prediction; Benchmark (surveying); Data set; Algorithm; Structural alignment; Combinatorics; Mathematics; Computer science; Pattern recognition (psychology); Crystallography; Biology; Sequence alignment; Artificial intelligence; Chemistry; Peptide sequence; Geography; Cartography","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":[0.0006347933,0.0001698713,0.0002551035,0.0005537426,0.0001744017,0.0004013536,0.0003294098,0.0003239528,0.0009709386],"category_scores_gemma":[0.001463265,0.0001372907,0.0002122914,0.0003834873,0.0004498198,0.0003941508,0.0001657286,0.0004902271,0.0001230109],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005328501,"about_ca_system_score_gemma":0.0002000421,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007998507,"about_ca_topic_score_gemma":0.000954462,"domain_scores_codex":[0.999759,0.00005630883,0.000008895851,0.00008380755,0.00006745258,0.00002450935],"domain_scores_gemma":[0.9982576,0.001023191,0.0003043638,0.0001387293,0.0001614616,0.0001146489],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0007165352,0.00009097091,0.007442616,0.00009191983,0.00005713711,0.00003428826,0.00003944331,0.005853175,0.9719296,0.001349796,0.00009184402,0.0123027],"study_design_scores_gemma":[0.00008488828,0.0009234502,0.2220594,0.0000143177,0.0001357447,0.0006816559,0.00008906564,0.2746956,0.4969453,0.003042464,0.001263084,0.00006500509],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9817467,0.000201147,0.01743178,0.00003541751,0.000005967415,0.000006956093,0.0001863145,0.00005973488,0.0003260233],"genre_scores_gemma":[0.9946458,0.00005234201,0.004641553,0.00001287824,0.000005647942,0.000008973025,0.0003144237,0.00002025105,0.0002979511],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009709386,"threshold_uncertainty_score":0.003866136,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0049530132775218,"score_gpt":0.2277748910401691,"score_spread":0.2228218777626473,"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."}}