{"id":"W3009958360","doi":"10.2196/16948","title":"Semantic Deep Learning: Prior Knowledge and a Type of Four-Term Embedding Analogy to Acquire Treatments for Well-Known Diseases","year":2020,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Analogy; Pairwise comparison; Artificial intelligence; Natural language processing; Computer science; Embedding; Deep learning; Term (time); Information retrieval; Linguistics","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":[0.0001088395,0.0001401718,0.0002669762,0.00003900323,0.00007394512,0.00001318505,0.0001795778,0.0002408321,0.00003654645],"category_scores_gemma":[0.001054545,0.0001086555,0.00006765094,0.0001163559,0.0001630367,0.000004998272,0.0001944076,0.0001141516,0.0000143612],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007755021,"about_ca_system_score_gemma":0.0001084706,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":8.723605e-7,"about_ca_topic_score_gemma":0.000003144043,"domain_scores_codex":[0.9990624,0.00003437119,0.0003530173,0.0001387784,0.0001802206,0.0002312674],"domain_scores_gemma":[0.9991773,0.00009924472,0.0001035341,0.0001193876,0.00007999807,0.0004205787],"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":[0.001263581,0.0005889716,0.03442233,0.003667445,0.0009286466,0.00002708039,0.0275706,0.00004311193,0.007590246,0.0001442912,0.02231493,0.9014388],"study_design_scores_gemma":[0.01255345,0.02128257,0.03790009,0.001153635,0.000883346,0.0001470029,0.01258604,0.1129016,0.01031234,0.000145721,0.7883238,0.001810423],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.987976,0.0009544355,0.009502744,0.0007301464,0.0001095723,0.0003706349,0.00001424466,0.0000360712,0.0003061692],"genre_scores_gemma":[0.9948496,0.0002836045,0.003496285,0.0008488839,0.0001913364,0.00004415726,0.0001023433,0.0000143229,0.0001694695],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8996283,"threshold_uncertainty_score":0.4430842,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02776214794007408,"score_gpt":0.34129162653838,"score_spread":0.3135294785983059,"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."}}