{"id":"W2165034468","doi":"10.1093/nar/gku956","title":"Xenbase, the Xenopus model organism database; new virtualized system, data types and genomes","year":2014,"lang":"en","type":"article","venue":"Nucleic Acids Research","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":138,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute of Child Health and Human Development; National Institutes of Health","keywords":"Xenopus; Biology; Genome; Computational biology; Model organism; Organism; Database; Gene; Genetics; Computer science","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":[0.001623073,0.001851495,0.001674821,0.004328862,0.0007444935,0.003741007,0.003292341,0.001235545,0.02778724],"category_scores_gemma":[0.003917443,0.001090606,0.001005135,0.005038372,0.0004480089,0.00330654,0.002397023,0.001555921,0.02730521],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001186887,"about_ca_system_score_gemma":0.002335465,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005345626,"about_ca_topic_score_gemma":0.004489478,"domain_scores_codex":[0.9992575,0.0001085102,0.0001729065,0.0001812205,0.0002084097,0.00007127245],"domain_scores_gemma":[0.9986467,0.0002552425,0.0002298043,0.0003663153,0.0001964355,0.0003056461],"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":[0.002601236,0.0001159573,0.003768253,0.004146996,0.0003024057,0.0006968773,0.0003440235,0.003650509,0.02918677,0.01626714,0.8528714,0.08604848],"study_design_scores_gemma":[0.0002711902,0.00007068618,0.0034726,0.000398227,0.0001686256,0.0005450219,0.0001066496,0.00377351,0.01338167,0.005212692,0.9724814,0.0001176415],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"software","genre_scores_codex":[0.004121167,0.002565105,0.04787846,0.000595026,0.0003589464,0.0002771147,0.824362,0.108713,0.01112927],"genre_scores_gemma":[0.007544547,0.001409235,0.03363282,0.0002481856,0.00004824291,0.0003642377,0.9468043,0.007303254,0.002645095],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.02778724,"threshold_uncertainty_score":0.09295756,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09007029162540417,"score_gpt":0.361542129770755,"score_spread":0.2714718381453508,"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."}}