{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01382511,0.0009012875,0.0007783737,0.01329198,0.002091387,0.005039379,0.0023338,0.00164438,0.01050513],"category_scores_gemma":[0.04097514,0.0007265522,0.002047764,0.009254646,0.003300218,0.01477464,0.003716777,0.002632531,0.002769893],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007188031,"about_ca_system_score_gemma":0.007607778,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0113398,"about_ca_topic_score_gemma":0.01224564,"domain_scores_codex":[0.9894561,0.002020232,0.002542311,0.001784961,0.003886211,0.0003101165],"domain_scores_gemma":[0.9703158,0.01241699,0.003479976,0.005459968,0.007637217,0.0006900502],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001059094,0.00008048053,0.007447727,0.002876746,0.00009718514,0.0003180573,0.007647546,0.002555283,0.005869172,0.7055801,0.04332407,0.2240977],"study_design_scores_gemma":[0.0000237339,0.00002870378,0.004512276,0.001416418,0.0001138518,0.0005482938,0.001717159,0.004003175,0.003659935,0.1407283,0.8431445,0.0001037748],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01643413,0.003638156,0.8468364,0.007972748,0.0006174949,0.00234434,0.02711373,0.004990269,0.09005272],"genre_scores_gemma":[0.1000939,0.003526709,0.8394955,0.001728884,0.0003094566,0.003707864,0.03630066,0.001541102,0.01329587],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01382511,"threshold_uncertainty_score":0.07311499,"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."}}