{"id":"W2024038403","doi":"10.1093/nar/gku1068","title":"BRENDA in 2015: exciting developments in its 25th year of existence","year":2014,"lang":"en","type":"article","venue":"Nucleic Acids Research","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":195,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Bundesministerium für Bildung und Forschung","keywords":"Annotation; Information retrieval; Relevance (law); Computer science; KEGG; Computational biology; Function (biology); Ontology; Biology; Data mining; Artificial intelligence; Gene ontology; Biochemistry; Genetics; Gene","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":[0.001444847,0.00007578718,0.0001412699,0.0001553332,0.00003383825,0.00001016197,0.0003289154,0.000176765,0.00002890826],"category_scores_gemma":[0.001146906,0.00006958804,0.00002302319,0.0003030684,0.0002089659,0.000002869875,0.0002619815,0.0002445514,0.00003178116],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002528729,"about_ca_system_score_gemma":0.00009928043,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006241202,"about_ca_topic_score_gemma":0.00009028364,"domain_scores_codex":[0.998611,0.000198291,0.0002242023,0.0002727764,0.0003102626,0.0003834374],"domain_scores_gemma":[0.999558,0.00004999159,0.000032327,0.0002040625,0.00009150401,0.00006408189],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.000278809,0.0002781193,0.3164943,0.0002204342,0.00002786573,0.00002500905,0.001178064,0.000008794807,0.4934978,0.0008845124,0.004088063,0.1830182],"study_design_scores_gemma":[0.003082321,0.001035499,0.7323131,0.000462435,0.000002575348,0.00001598723,0.002428278,0.0008143007,0.1733699,0.0005628358,0.08542038,0.0004923853],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9913368,0.0002746672,0.00005623653,0.0001381297,0.00003930956,0.00007891715,0.000003013995,0.00000588057,0.008067098],"genre_scores_gemma":[0.9961373,0.0001189037,0.003067149,0.00002745531,0.0000489669,0.00001161566,0.00001004513,0.00001051711,0.0005680675],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4158187,"threshold_uncertainty_score":0.2837718,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06924922146140024,"score_gpt":0.3853085447481472,"score_spread":0.316059323286747,"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."}}