{"id":"W2760618582","doi":"10.1186/s13326-017-0153-x","title":"Semantic annotation in biomedicine: the current landscape","year":2017,"lang":"en","type":"review","venue":"Journal of Biomedical Semantics","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":78,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Biomedicine; Annotation; Current (fluid); Data science; Information retrieval; Semantic annotation; Natural language processing; Artificial intelligence; Bioinformatics; Biology","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.006776706,0.001112117,0.001786311,0.006549703,0.0009685637,0.004458873,0.002350995,0.003723679,0.003177388],"category_scores_gemma":[0.01018462,0.0005135479,0.0008261265,0.009739309,0.004786978,0.009566657,0.003349534,0.003365882,0.002323289],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00256395,"about_ca_system_score_gemma":0.006381103,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003382417,"about_ca_topic_score_gemma":0.002677995,"domain_scores_codex":[0.997474,0.0008886267,0.0003292334,0.0003434113,0.000863275,0.0001013468],"domain_scores_gemma":[0.9879418,0.00931477,0.0005761648,0.0005042369,0.001398212,0.0002648937],"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":[0.00004074517,0.00004910153,0.0004607146,0.01925975,0.00007712131,0.0001671915,0.0008611647,0.0005746874,0.001029539,0.05212199,0.02723002,0.8981279],"study_design_scores_gemma":[0.000006654449,0.00002918718,0.000816343,0.009287884,0.00007170156,0.0007871726,0.0006665394,0.0005089428,0.0006811025,0.03562067,0.9514818,0.000042067],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0003454375,0.9832683,0.006288335,0.005293353,0.0007253502,0.00001997333,0.00006369736,0.00008751333,0.003908054],"genre_scores_gemma":[0.002767323,0.9877523,0.006076669,0.001616225,0.0008156365,0.00003004836,0.0001244489,0.00002789081,0.0007894224],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.006776706,"threshold_uncertainty_score":0.03583908,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08133893116983922,"score_gpt":0.4133682515996607,"score_spread":0.3320293204298215,"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."}}