{"id":"W2143092506","doi":"10.1002/prot.20677","title":"A new catalog of protein β‐sheets","year":2005,"lang":"en","type":"article","venue":"Proteins Structure Function and Bioinformatics","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Canadian Institutes of Health Research","keywords":"Spider; Folding (DSP implementation); Annotation; Computer science; Computational biology; Protein folding; Domain (mathematical analysis); Set (abstract data type); Polypeptide chain; Protein structure; Structural motif; Crystallography; Structural alignment; Chemistry; Amino acid; Biology; Peptide sequence; Artificial intelligence; Biochemistry; Mathematics; Sequence alignment; Engineering; Programming language; Structural engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006882687,0.0001980531,0.0001722727,0.00007232798,0.00007755135,0.00002912286,0.0001218989,0.000223761,0.00007168933],"category_scores_gemma":[0.00004553837,0.0001591978,0.00005940951,0.0001141305,0.00006896305,0.00002243576,0.00009649169,0.000124865,0.000004163329],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001092698,"about_ca_system_score_gemma":0.0001046357,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001907531,"about_ca_topic_score_gemma":0.00006918175,"domain_scores_codex":[0.9990958,0.00001448849,0.0003461241,0.0001717249,0.0001711946,0.0002006976],"domain_scores_gemma":[0.9993099,0.000002554716,0.0001919675,0.0002939962,0.00008015314,0.0001214385],"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":[0.0003837585,0.00002646507,0.0006244344,0.000293835,0.0001120049,3.857086e-7,0.0002507864,0.0001118859,0.599335,0.003457668,0.001260394,0.3941434],"study_design_scores_gemma":[0.004048275,0.002187832,0.006394247,0.0001225443,0.0001251498,0.0001854318,0.0002481889,0.003911769,0.8214745,0.007397369,0.1527606,0.001144123],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6118919,0.001726147,0.3782916,0.0005728806,0.0003217879,0.002260814,0.0001991811,0.00008726222,0.004648362],"genre_scores_gemma":[0.8690611,0.00002686022,0.1296524,0.000253089,0.0003158743,0.00001219675,0.000178966,0.00001514784,0.0004843512],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3929993,"threshold_uncertainty_score":0.6491897,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003820180209504846,"score_gpt":0.1984477464750891,"score_spread":0.1946275662655843,"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."}}