{"id":"W4292850386","doi":"10.1101/2022.08.22.504886","title":"RiboXYZ: A comprehensive database for visualizing and analyzing ribosome structures","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"RNA and protein synthesis mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Ribosome; Protein Data Bank (RCSB PDB); Computer science; Identification (biology); Protein Data Bank; Set (abstract data type); Computational biology; Visualization; Ribosomal RNA; Protein structure database; Database; Protein structure; Data mining; Biology; RNA; Programming language; Sequence database; Genetics; Biochemistry","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":[0.002220683,0.003088886,0.002361416,0.005840218,0.0008843156,0.004193999,0.004320001,0.002105679,0.02853779],"category_scores_gemma":[0.005660107,0.00164095,0.00157542,0.00505661,0.000541073,0.003283089,0.004987402,0.002045817,0.03557395],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007981893,"about_ca_system_score_gemma":0.001875447,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002341346,"about_ca_topic_score_gemma":0.002872345,"domain_scores_codex":[0.998722,0.0001794395,0.0002022124,0.0002497456,0.0005057568,0.0001409087],"domain_scores_gemma":[0.9987038,0.0004134457,0.0001611303,0.0003545047,0.0002084457,0.0001587015],"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":[0.001496726,0.0002123929,0.00293265,0.007491113,0.0004059676,0.001163116,0.0006579342,0.008087668,0.04577444,0.02077132,0.7426908,0.1683159],"study_design_scores_gemma":[0.0007651443,0.0001079006,0.006889719,0.000900178,0.0002245343,0.001567585,0.0002544833,0.02454776,0.03366878,0.02206821,0.9085974,0.0004082584],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"software","genre_scores_codex":[0.0136429,0.006542453,0.1706361,0.0005693085,0.0002881532,0.0003831603,0.4370492,0.3569678,0.0139208],"genre_scores_gemma":[0.0318986,0.004639606,0.1523705,0.0003779665,0.00009394839,0.0009807,0.7668526,0.03764024,0.0051458],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.02853779,"threshold_uncertainty_score":0.0954684,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02073811379290134,"score_gpt":0.2640011539404898,"score_spread":0.2432630401475885,"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."}}