{"id":"W4385893888","doi":"10.18653/v1/2023.bionlp-1.25","title":"Extracting Drug-Drug and Protein-Protein Interactions from Text using a Continuous Update of Tree-Transformers","year":2023,"lang":"en","type":"article","venue":"","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Sentence; Computer science; Transformer; Artificial intelligence; Natural language processing; Tree (set theory); Mathematics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0007019945,0.001186977,0.000569378,0.002054097,0.0003093719,0.001018065,0.001158559,0.0008902493,0.002579552],"category_scores_gemma":[0.003684717,0.0004617797,0.001390943,0.001719251,0.0004122727,0.003218862,0.0009020607,0.001332304,0.002289423],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007049439,"about_ca_system_score_gemma":0.001409703,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004096543,"about_ca_topic_score_gemma":0.008401112,"domain_scores_codex":[0.9995185,0.00008860901,0.00005818108,0.0001840255,0.0001190961,0.00003156139],"domain_scores_gemma":[0.9984373,0.0009228844,0.0001340516,0.0001348736,0.0003140663,0.00005676215],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007477162,0.0003600242,0.00742916,0.0008445531,0.000203574,0.001234979,0.0006667256,0.1080934,0.04069011,0.01438698,0.02647014,0.7988726],"study_design_scores_gemma":[0.00004167698,0.0001382999,0.001715131,0.0000439502,0.0001291374,0.0003924092,0.0001131296,0.9568878,0.01154394,0.01812145,0.01083882,0.00003422518],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06885529,0.001759382,0.9036408,0.001653149,0.0003335497,0.0004758521,0.007475786,0.0112787,0.004527453],"genre_scores_gemma":[0.506801,0.001738286,0.466931,0.0005036239,0.0002679384,0.0004686629,0.01698216,0.0005916588,0.005715644],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004096543,"threshold_uncertainty_score":0.008629441,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01771311462866948,"score_gpt":0.2848994085851069,"score_spread":0.2671862939564374,"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."}}