{"id":"W2773351420","doi":"10.2196/medinform.7059","title":"Search and Graph Database Technologies for Biomedical Semantic Indexing: Experimental Analysis","year":2017,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Search engine indexing; Information retrieval; Graph database; Graph; Database; Theoretical computer science","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":[0.0005457975,0.0001690698,0.0002889897,0.0001740852,0.0003613039,0.000103928,0.0006792115,0.0004928861,0.00002095362],"category_scores_gemma":[0.0008109458,0.0001283029,0.0001229776,0.0001267003,0.001601766,0.00001403021,0.0007451217,0.0002218483,0.000004061535],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008425263,"about_ca_system_score_gemma":0.00009246374,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001569871,"about_ca_topic_score_gemma":0.00001257892,"domain_scores_codex":[0.9985502,0.00001942482,0.0003848146,0.0002086889,0.0004701322,0.0003667847],"domain_scores_gemma":[0.9988706,0.0000485965,0.0001214424,0.0006699778,0.00005027949,0.0002391484],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008894175,0.002023276,0.05594824,0.002070076,0.005205124,0.0001219458,0.006606491,0.000007404039,0.04530315,0.00395102,0.05813655,0.8197373],"study_design_scores_gemma":[0.01896378,0.007319022,0.01721149,0.000830989,0.001366741,0.0004265638,0.0724446,0.1052573,0.2721625,0.001264507,0.4989522,0.003800359],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9641218,0.0005851799,0.03299655,0.001394791,0.0001418299,0.0003094448,0.00007034966,0.00009764588,0.0002823888],"genre_scores_gemma":[0.987035,0.0002247966,0.01193766,0.0002515725,0.0001002675,0.00009351991,0.0002640746,0.00001025925,0.00008279017],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.815937,"threshold_uncertainty_score":0.5901772,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03028209886436811,"score_gpt":0.3609085883083696,"score_spread":0.3306264894440015,"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."}}