{"id":"W4378901016","doi":"10.2196/45496","title":"Interoperable, Domain-Specific Extensions for the German Corona Consensus (GECCO) COVID-19 Research Data Set Using an Interdisciplinary, Consensus-Based Workflow: Data Set Development Study","year":2023,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Interoperability; Workflow; Computer science; Data science; Domain (mathematical analysis); USable; World Wide Web; Database","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006490193,0.0002932358,0.0003656617,0.0001702773,0.00113873,0.0001442662,0.002963556,0.0003735354,0.00006743374],"category_scores_gemma":[0.001464471,0.0001960664,0.00005056286,0.0004777263,0.001404948,0.00001379975,0.006193709,0.0006334718,0.00004394653],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008639583,"about_ca_system_score_gemma":0.00168547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003170831,"about_ca_topic_score_gemma":0.0003917515,"domain_scores_codex":[0.9961081,0.0004172758,0.001013535,0.0005914865,0.001090529,0.000779059],"domain_scores_gemma":[0.9947348,0.001265014,0.0001813147,0.002926265,0.0002345424,0.0006580459],"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":[0.0008842391,0.0007292901,0.0006373709,0.0003353119,0.0003938857,0.0002037017,0.01796698,0.0001357138,0.0006130267,0.00003127867,0.8976918,0.08037733],"study_design_scores_gemma":[0.002663377,0.0007539177,0.0005470297,0.0001601886,0.00003608706,0.0001156434,0.06002589,0.1163417,0.00009428461,0.0001040422,0.818751,0.0004069195],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9640609,0.0003057786,0.02675117,0.005086106,0.0006184454,0.001844225,0.001121557,0.0001530775,0.00005875043],"genre_scores_gemma":[0.9326,0.0001703309,0.04383444,0.003887047,0.0007137497,0.000338027,0.01806163,0.00009287342,0.0003019398],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1162059,"threshold_uncertainty_score":0.8758304,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3909576814881041,"score_gpt":0.5202761976731551,"score_spread":0.129318516185051,"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."}}