{"id":"W2170041483","doi":"10.1093/sysbio/sys025","title":"NeXML: Rich, Extensible, and Verifiable Representation of Comparative Data and Metadata","year":2012,"lang":"en","type":"article","venue":"Systematic Biology","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":111,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; University of British Columbia","funders":"FP7 People: Marie-Curie Actions; Natural Sciences and Engineering Research Council of Canada; National Evolutionary Synthesis Center; National Science Foundation","keywords":"Computer science; XML; Metadata; Python (programming language); Interoperability; Data exchange; Software; External Data Representation; File format; Software engineering; Data science; Programming language; World Wide Web","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":[0.0003497027,0.00009583581,0.0003478848,0.00002453841,0.00004023885,0.000009873495,0.0001355466,0.00006983558,0.000001459854],"category_scores_gemma":[0.0001019066,0.00007352496,0.00001439975,0.0000424372,0.000134198,0.0000034947,0.0004981636,0.0000261987,8.500813e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000001660155,"about_ca_system_score_gemma":0.00001122161,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003792411,"about_ca_topic_score_gemma":0.0000116361,"domain_scores_codex":[0.9991768,0.0001603296,0.0002576481,0.000233512,0.00003311308,0.000138576],"domain_scores_gemma":[0.9991699,0.00005593804,0.0001500747,0.0005325445,0.00004901501,0.00004253857],"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":[0.00003728466,0.00005499034,0.1197165,0.002977125,0.0005468029,2.165879e-7,0.0007191607,0.000005170325,0.8721659,0.003405287,0.0002892189,0.00008227583],"study_design_scores_gemma":[0.005885405,0.002385011,0.3679985,0.00170162,0.002514571,0.0005553658,0.02317404,0.005199569,0.5755853,0.007852618,0.004818574,0.002329405],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9770043,0.02091157,0.001326297,0.00002476385,0.0001058219,0.0003190982,0.00008053135,0.000001504184,0.0002261416],"genre_scores_gemma":[0.9970059,0.0003649621,0.002392768,0.00002998904,0.00005093954,0.00001773605,0.0000965366,0.000004849218,0.00003628765],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2965806,"threshold_uncertainty_score":0.2998261,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08421342510478297,"score_gpt":0.3356246268713076,"score_spread":0.2514112017665247,"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."}}