{"id":"W1598794494","doi":"10.1002/dvg.22873","title":"Xenbase: Core features, data acquisition, and data processing","year":2015,"lang":"en","type":"article","venue":"genesis","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; Biotechnology and Biological Sciences Research Council; National Institutes of Health; Wellcome Trust","keywords":"Core (optical fiber); Computer science; Data acquisition; Telecommunications; Operating system","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003146031,0.00009402219,0.00009500681,0.00001738364,0.00006710353,0.00003546045,0.0006304362,0.0001339201,0.000006794871],"category_scores_gemma":[0.000236993,0.00007779257,0.000007838815,0.0000623142,0.0001300009,0.000006341852,0.001054543,0.00004271703,0.000005155997],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003587737,"about_ca_system_score_gemma":0.0001141134,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004438834,"about_ca_topic_score_gemma":0.0000386047,"domain_scores_codex":[0.9991285,0.00002431873,0.00009668801,0.0004775594,0.0001180453,0.0001549204],"domain_scores_gemma":[0.9986962,0.000007664249,0.00003989786,0.001086518,0.00005209774,0.0001175994],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008807914,0.00004795988,0.006002987,0.00005245411,0.0000454198,0.00001510694,0.00008074239,0.000001571961,0.02612199,0.00000942299,0.4776064,0.4899279],"study_design_scores_gemma":[0.001037413,0.0002227216,0.02420087,0.00004383992,0.00009287593,0.0001918979,0.0007127124,0.0008159648,0.006812894,0.0001574212,0.9652831,0.0004283111],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8481904,0.1333809,0.0111564,0.003592869,0.0005267095,0.0002542335,0.001396973,0.0001426506,0.001358844],"genre_scores_gemma":[0.9603483,0.0007198285,0.02824599,0.001143992,0.0007981906,0.000006915396,0.007742789,0.00002684172,0.0009671106],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4894996,"threshold_uncertainty_score":0.3172289,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1302921301307467,"score_gpt":0.3598876030192796,"score_spread":0.2295954728885329,"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."}}