{"id":"W2902428389","doi":"10.3897/bdj.6.e29232","title":"Modifier Ontologies for frequency, certainty, degree, and coverage phenotype modifier","year":2018,"lang":"en","type":"article","venue":"Biodiversity Data Journal","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Government of Canada; Agriculture and Agri-Food Canada","funders":"Birmingham Biomedical Research Centre; Imperial Experimental Cancer Medicine Centre; Horizon 2020 Framework Programme; Medical Research Council; Surgical Reconstruction and Microbiology Research Centre; National Institute for Health and Care Research; National Science Foundation","keywords":"Computer science; Ontology; Set (abstract data type); Information retrieval; Class (philosophy); Object (grammar); Interval (graph theory); Degree (music); Certainty; Data mining; Theoretical computer science; Artificial intelligence; Mathematics; Programming language","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":[0.0003752547,0.0001441299,0.0001616024,0.00004648892,0.0004429636,0.00008794391,0.0006711819,0.0002254375,0.00005410324],"category_scores_gemma":[0.0004700462,0.0001190172,0.00005222105,0.00004472752,0.0005171545,0.00001788106,0.0006386148,0.0001357548,0.00001985115],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001590956,"about_ca_system_score_gemma":0.00008997058,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007857901,"about_ca_topic_score_gemma":0.0001070016,"domain_scores_codex":[0.9989845,0.00004984145,0.0001514699,0.0003712193,0.0001430306,0.0002999604],"domain_scores_gemma":[0.9991198,0.00003796416,0.0001012724,0.0004492017,0.0001480433,0.0001437209],"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":[0.001063237,0.0001787857,0.04495282,0.00005327455,0.0005164491,0.00004669248,0.0003619562,0.000001925723,0.0270087,0.0001869944,0.7075028,0.2181263],"study_design_scores_gemma":[0.004944626,0.00302615,0.04827746,0.00004474893,0.0002796653,0.0004324215,0.001219818,0.0004449639,0.005991625,0.003248609,0.931103,0.0009869203],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9326533,0.002858543,0.05716632,0.002028878,0.001081522,0.0002455574,0.002591452,0.00004298291,0.001331437],"genre_scores_gemma":[0.9636058,0.001250461,0.0315151,0.001183549,0.001094352,0.000002481973,0.0009107848,0.00001118835,0.000426244],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2236001,"threshold_uncertainty_score":0.4853382,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1240046624962398,"score_gpt":0.3116825664381551,"score_spread":0.1876779039419154,"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."}}