{"id":"W192939834","doi":"10.4137/bbi.s451","title":"Ontologies for Bioinformatics","year":2008,"lang":"en","type":"article","venue":"Bioinformatics and Biology Insights","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Interoperability; Computer science; Ontology; Data science; Context (archaeology); Semantic interoperability; Open Biomedical Ontologies; Semantics (computer science); Semantic Web; Meaning (existential); IDEF5; Semantic integration; World Wide Web; Knowledge management; Upper ontology; Semantic Web Stack; Ontology alignment; Biology","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":[0.0001167598,0.0002000515,0.0002457934,0.00007865222,0.0002886803,0.00001646802,0.0001932656,0.0003822249,0.000003454302],"category_scores_gemma":[0.0002123787,0.0001388961,0.00008468071,0.00006791532,0.000626382,0.000007955406,0.0001322717,0.00007972591,0.00001029304],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000649652,"about_ca_system_score_gemma":0.00006991281,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005271214,"about_ca_topic_score_gemma":0.00001171077,"domain_scores_codex":[0.9990515,0.00001797237,0.0003869118,0.0001742844,0.00005534839,0.0003139847],"domain_scores_gemma":[0.9993705,0.00006152222,0.0001372892,0.0002435481,0.00008509852,0.0001020383],"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":[0.001438265,0.000604538,0.02624664,0.001144315,0.001282142,0.0000189312,0.01083393,0.00002327893,0.06317588,0.03116385,0.1640023,0.7000659],"study_design_scores_gemma":[0.002197662,0.002966756,0.003452272,0.00002847155,0.00003910933,0.0003007127,0.001079902,0.005512317,0.01068494,0.001983337,0.971037,0.0007174746],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.928537,0.003651698,0.06069596,0.0004934528,0.0006310917,0.0005696088,0.0001002222,0.0001316344,0.005189335],"genre_scores_gemma":[0.8294421,0.003332134,0.1641949,0.001512274,0.0003040773,0.00007934434,0.0004909388,0.00002028433,0.0006239114],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8070347,"threshold_uncertainty_score":0.5664017,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05290572275187795,"score_gpt":0.2885351413558813,"score_spread":0.2356294186040034,"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."}}