{"id":"W2120235270","doi":"10.1002/j.0022-0337.2011.75.1.tb05024.x","title":"The Development of a Dental Diagnostic Terminology","year":2011,"lang":"en","type":"article","venue":"Journal of Dental Education","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":88,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"U.S. National Library of Medicine; National Institute of Dental and Craniofacial Research","keywords":"Terminology; Medical diagnosis; Standardization; Quality assurance; Work (physics); Medicine; Medical physics; Medical education; Computer science; Pathology; Linguistics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001642272,0.00004950458,0.0000722577,0.00003175082,0.00005042176,0.000004947465,0.0001688622,0.00006264011,0.00001761827],"category_scores_gemma":[0.0003359334,0.00003218823,0.00004755381,0.0000322984,0.0001127441,0.000002438142,0.00003823774,0.00005904103,0.000005458075],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001556643,"about_ca_system_score_gemma":0.000311453,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004492457,"about_ca_topic_score_gemma":0.00003362005,"domain_scores_codex":[0.9994678,0.00002885654,0.0002765349,0.00005516474,0.00008934468,0.0000822534],"domain_scores_gemma":[0.9995494,0.0000343831,0.0002319355,0.00007596664,0.00006689633,0.00004140454],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002226034,0.0008185306,0.1295928,0.00002585093,0.0001677341,0.000005189765,0.00259542,2.391045e-7,0.1238779,0.00007514816,0.006539274,0.7360793],"study_design_scores_gemma":[0.0005598467,0.0008591035,0.4121024,0.00008160951,0.00004891291,0.0006439589,0.01242307,0.000001184442,0.5238571,0.0002246084,0.04905593,0.000142253],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9968321,0.001470621,0.0002243512,0.00003981018,0.0008533112,0.00003274115,5.842167e-7,0.00000115769,0.0005452874],"genre_scores_gemma":[0.9948391,0.000106518,0.004614765,0.00003639581,0.000126506,0.000003353925,0.000003264368,0.000003638799,0.0002664632],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7359371,"threshold_uncertainty_score":0.1312598,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02158938240088802,"score_gpt":0.294003046089091,"score_spread":0.272413663688203,"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."}}