{"id":"W2402912048","doi":"10.3233/978-1-61499-432-9-1125","title":"Addressing the Challenge of Encoding Causal Epidemiological Knowledge in Formal Ontologies: A Practical Perspective","year":2014,"lang":"en","type":"article","venue":"Studies in health technology and informatics","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University Health Centre","funders":"","keywords":"Perspective (graphical); Computer science; Encoding (memory); Data science; Knowledge management; Population; Public health; Formal description; Artificial intelligence; Medicine; Environmental health; Programming language; Pathology","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.03394138,0.001198107,0.001780119,0.005105633,0.002773213,0.01436849,0.005462444,0.005116789,0.002990745],"category_scores_gemma":[0.08318567,0.001494666,0.002684728,0.007497857,0.009136842,0.02647317,0.008958442,0.008045345,0.0007590225],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004058223,"about_ca_system_score_gemma":0.006189337,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00855074,"about_ca_topic_score_gemma":0.006094531,"domain_scores_codex":[0.9799604,0.01243506,0.002234483,0.00129004,0.003512694,0.0005673271],"domain_scores_gemma":[0.8957983,0.08460241,0.004802596,0.009642813,0.004239986,0.0009139458],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00003385112,0.00005432974,0.0006149934,0.0006066359,0.0001037874,0.0004082773,0.001295572,0.02150988,0.0004821547,0.9330217,0.002759553,0.03910924],"study_design_scores_gemma":[0.00001423815,0.0000148989,0.0000761995,0.0002945513,0.00004870368,0.0001906464,0.0008333735,0.02712293,0.0006741766,0.9481908,0.02251409,0.00002535684],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001993159,0.000829266,0.9774708,0.01624814,0.0001455252,0.00009120598,0.0003735173,0.0003375356,0.002510803],"genre_scores_gemma":[0.1038776,0.003137215,0.8879261,0.002097969,0.0005009106,0.0002454978,0.0009167033,0.0001595247,0.001138592],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9660586,"threshold_uncertainty_score":0.1795013,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2107657806240874,"score_gpt":0.4702990112289366,"score_spread":0.2595332306048492,"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."}}