{"id":"W2121237412","doi":"10.1093/nar/gkq1173","title":"Towards BioDBcore: a community-defined information specification for biological databases","year":2010,"lang":"en","type":"editorial","venue":"Nucleic Acids Research","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Institute for Cancer Research","funders":"National Institute of General Medical Sciences; Natural Environment Research Council; National Human Genome Research Institute; Biotechnology and Biological Sciences Research Council; National Institutes of Health","keywords":"Interoperability; Scope (computer science); Relevance (law); Consistency (knowledge bases); Database; Resource (disambiguation); Biology; Semantic heterogeneity; Knowledge management; Data science; Computer science; World Wide Web; Semantic Web","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":["metaresearch","research_integrity"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.003490733,0.0002943438,0.0003574139,0.000245355,0.0006126293,0.0001453032,0.001234357,0.002198519,0.00006490697],"category_scores_gemma":[0.01576735,0.0002371964,0.0001595567,0.0002587881,0.00100237,0.00001216716,0.0007904887,0.002804453,0.0001088642],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005972543,"about_ca_system_score_gemma":0.0006459102,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002813228,"about_ca_topic_score_gemma":0.00012217,"domain_scores_codex":[0.9970847,0.0005441921,0.0004632232,0.0004249568,0.0008414162,0.0006414777],"domain_scores_gemma":[0.9965267,0.0008586904,0.0001657539,0.001176624,0.001098318,0.0001738743],"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":[0.0003700461,0.0001047657,0.00002101897,0.0001196536,0.00004177928,6.201751e-7,0.00009700639,7.217321e-8,0.01660533,0.00009278061,0.9195616,0.06298535],"study_design_scores_gemma":[0.0006796197,0.001135234,0.0002032958,0.00004077335,0.00001114523,0.000002172992,0.0003781061,0.00001494603,0.003724103,0.0002491356,0.9932875,0.0002739906],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.07832059,0.002090144,0.02073098,0.003197215,0.8666638,0.004119905,0.01410771,0.0004730193,0.01029664],"genre_scores_gemma":[0.02783049,0.003697717,0.03450245,0.0002290283,0.826025,0.0008414731,0.1049502,0.0001363525,0.001787295],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.09084246,"threshold_uncertainty_score":0.9994961,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1254557582394825,"score_gpt":0.4057446548960152,"score_spread":0.2802888966565327,"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."}}