{"id":"W2921069034","doi":"10.1016/j.prevetmed.2019.03.002","title":"Drivers for the development of an Animal Health Surveillance Ontology (AHSO)","year":2019,"lang":"en","type":"article","venue":"Preventive Veterinary Medicine","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Prince Edward Island","funders":"VINNOVA","keywords":"Interoperability; Ontology; Computer science; Animal health; Field (mathematics); Domain (mathematical analysis); Knowledge management; Data science; Component (thermodynamics); Semantic Web; Set (abstract data type); Semantic interoperability; World Wide Web; Medicine","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":[0.0007205761,0.0001110968,0.0002389933,0.00002739104,0.00006934837,0.000001589511,0.0002324769,0.0000751612,0.0000393979],"category_scores_gemma":[0.0001152362,0.00007138239,0.00004438719,0.00005192985,0.0002368924,0.000002125448,0.00008890891,0.00005632561,0.00000226829],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000137268,"about_ca_system_score_gemma":0.0001113487,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001916498,"about_ca_topic_score_gemma":0.00004463053,"domain_scores_codex":[0.9990289,0.0001303347,0.0002662971,0.0002594569,0.000105909,0.0002091034],"domain_scores_gemma":[0.9993898,0.0001013843,0.0001474679,0.0002444017,0.00005700535,0.00005994431],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001911023,0.0002399294,0.01143642,0.0003806248,0.0003617531,0.000003768606,0.002835024,0.000005756998,0.6732794,0.0001381606,0.002446003,0.3069621],"study_design_scores_gemma":[0.004515985,0.03502743,0.4070307,0.0002159759,0.00002931067,0.00008160116,0.003907715,0.0003143794,0.01806177,0.00009824558,0.5303116,0.0004053194],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9903891,0.001594124,0.005865483,0.001335023,0.0003048897,0.0003758434,0.00001189218,0.00001069224,0.0001129603],"genre_scores_gemma":[0.9922206,0.00006858619,0.006952986,0.000189593,0.0001070554,0.00003321024,0.00008455212,0.000009417691,0.0003340428],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6552176,"threshold_uncertainty_score":0.2910889,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04311994896015978,"score_gpt":0.3564570117521965,"score_spread":0.3133370627920368,"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."}}