{"id":"W2807391974","doi":"","title":"TCS Research at TAC 2017: Joint Extraction of Entities and Relations from Drug Labels using an Ensemble of Neural Networks.","year":2017,"lang":"en","type":"article","venue":"Theory and applications of categories","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial neural network; Joint (building); Extraction (chemistry); Artificial intelligence; Machine learning; Chemistry; Chromatography; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002753577,0.002584513,0.001571699,0.005277563,0.001318144,0.002583043,0.002251032,0.002260244,0.008895466],"category_scores_gemma":[0.007297501,0.0007000279,0.002010772,0.004085896,0.0004521624,0.003431906,0.001947812,0.003251352,0.00698232],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001679959,"about_ca_system_score_gemma":0.005014729,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02574236,"about_ca_topic_score_gemma":0.04593501,"domain_scores_codex":[0.9980403,0.0004943162,0.0001242865,0.0005062826,0.0006594557,0.0001753326],"domain_scores_gemma":[0.9970875,0.0009365871,0.0001546748,0.0006504785,0.0009038383,0.0002669474],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001217693,0.00161933,0.006371639,0.001158429,0.001159977,0.000617053,0.0002961741,0.02988203,0.01906748,0.006772873,0.5156507,0.4161866],"study_design_scores_gemma":[0.0005076424,0.000578539,0.006305721,0.0002579046,0.0008427018,0.0003659095,0.0004577906,0.7482213,0.03176649,0.03213811,0.1783702,0.0001877238],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1389209,0.01076475,0.4028509,0.008033823,0.003816491,0.001965228,0.2691601,0.1327429,0.03174495],"genre_scores_gemma":[0.1502089,0.00199782,0.45851,0.000742488,0.0005239455,0.001001601,0.3661824,0.002743458,0.0180894],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02574236,"threshold_uncertainty_score":0.05118501,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05898442574939388,"score_gpt":0.3593710565403336,"score_spread":0.3003866307909397,"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."}}