{"id":"W26197850","doi":"10.1007/s10654-015-0070-1","title":"Relation extraction from biomedical text","year":2007,"lang":"en","type":"dissertation","venue":"European Journal of Epidemiology","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Relationship extraction; Discriminative model; Artificial intelligence; Parsing; Generative grammar; Natural language processing; Generative model; Machine learning; Information extraction; Biomedical text mining; Sentence; Boosting (machine learning); Information retrieval; Text mining","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00515554,0.0002584111,0.0005922249,0.0002154982,0.00006664064,0.000007222812,0.0003828688,0.0006164722,0.0001457905],"category_scores_gemma":[0.00772432,0.0002081519,0.0003201922,0.00009327484,0.0001918767,0.000003973867,0.00003751703,0.0008620725,0.00007443257],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002806685,"about_ca_system_score_gemma":0.0001211078,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001402379,"about_ca_topic_score_gemma":0.00001702803,"domain_scores_codex":[0.9960029,0.001577223,0.001539942,0.0003603121,0.000192311,0.0003273187],"domain_scores_gemma":[0.9969845,0.0005683412,0.001747631,0.0002797583,0.0001981859,0.0002216102],"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":[0.001676231,0.0001868528,0.002592845,0.00004585548,0.0005529003,0.0003801581,0.0002541382,0.00002425223,0.1065302,0.0000724709,0.1540891,0.733595],"study_design_scores_gemma":[0.0007364986,0.001353366,0.1000651,0.0001992999,0.0001228399,0.0001914195,0.0003344686,0.00001547688,0.001549919,0.0004311088,0.8947235,0.0002769023],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8700408,0.02824176,0.07083707,0.0009917442,0.01195653,0.0001508419,0.00003566975,0.00003711111,0.01770852],"genre_scores_gemma":[0.8530785,0.008373907,0.0959547,0.002373853,0.0179149,0.00000341434,0.009230671,0.0002628591,0.01280717],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7406344,"threshold_uncertainty_score":0.924729,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04590250778340296,"score_gpt":0.37262066374105,"score_spread":0.326718155957647,"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."}}