{"id":"W4221090405","doi":"10.1186/s12859-022-04636-8","title":"The Xenopus phenotype ontology: bridging model organism phenotype data to human health and development","year":2022,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute of Child Health and Human Development; National Human Genome Research Institute; National Institutes of Health","keywords":"Ontology; Open Biomedical Ontologies; Computer science; Ontology-based data integration; Interoperability; Process ontology; Phenotype; Computational biology; Biology; Suggested Upper Merged Ontology; Bioinformatics; Information retrieval; World Wide Web; Semantic Web; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003493503,0.0006083588,0.0004404494,0.002212377,0.000856262,0.002224973,0.001480388,0.000883883,0.004304404],"category_scores_gemma":[0.004999762,0.0004514483,0.001306685,0.00198828,0.001485748,0.004082572,0.002236216,0.001443254,0.001497442],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001812246,"about_ca_system_score_gemma":0.003849513,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009337616,"about_ca_topic_score_gemma":0.01036439,"domain_scores_codex":[0.9984515,0.0004227312,0.0003302456,0.0002766597,0.0004394051,0.00007936932],"domain_scores_gemma":[0.9967254,0.001224357,0.0004565635,0.0009202005,0.0004834322,0.0001898504],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003537546,0.000149279,0.01266546,0.003121314,0.0002098487,0.001536599,0.004057304,0.009229808,0.03166692,0.6023992,0.09586968,0.2387408],"study_design_scores_gemma":[0.00004880265,0.00006999722,0.007621306,0.001114081,0.0001305083,0.001431946,0.0009546669,0.01432351,0.01752047,0.1067535,0.8499256,0.0001056199],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01575765,0.0009967863,0.9139767,0.004509814,0.0003991935,0.0006086031,0.02123747,0.01221425,0.03029956],"genre_scores_gemma":[0.1019357,0.002860151,0.8472241,0.001598376,0.0001502135,0.000908865,0.03467085,0.003279659,0.007372138],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009337616,"threshold_uncertainty_score":0.01856649,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07694043601688808,"score_gpt":0.3153713865619501,"score_spread":0.2384309505450621,"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."}}