{"id":"W2145428772","doi":"10.1186/1471-2105-12-481","title":"U-Compare bio-event meta-service: compatible BioNLP event extraction services","year":2011,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Brain Injury Research Center; U.S. National Library of Medicine; National Institute of General Medical Sciences; Precursory Research for Embryonic Science and Technology; National Institute on Drug Abuse; Directorate for Biological Sciences; National Institutes of Health; Fonds Wetenschappelijk Onderzoek; China Scholarship Council; Joint Information Systems Committee; Vlaamse regering; Biotechnology and Biological Sciences Research Council; University of Tokyo; Korea Institute of Science and Technology Information; University of Massachusetts Amherst; Korea Institute of Science and Technology; Norges Teknisk-Naturvitenskapelige Universitet; Japan Society for the Promotion of Science; Academy of Finland; Ministry of Education, Culture, Sports, Science and Technology; National Institute of Advanced Industrial Science and Technology","keywords":"Computer science; Event (particle physics); Biomedical text mining; Interoperability; Service (business); Task (project management); Data mining; Extraction (chemistry); Information extraction; Information retrieval; Text mining; World Wide Web; Systems engineering; Engineering","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":[0.0002858375,0.0002599872,0.0003118217,0.00008260984,0.0001301453,0.00003604261,0.0003866644,0.0002178363,0.0001992369],"category_scores_gemma":[0.00002849827,0.0001965795,0.0002159268,0.000191322,0.0000872649,0.00001665545,0.0001765851,0.0001247214,0.0002001177],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001644103,"about_ca_system_score_gemma":0.00007603686,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001356752,"about_ca_topic_score_gemma":0.0003969227,"domain_scores_codex":[0.9986082,0.00004863911,0.0005476548,0.0002189854,0.0002396448,0.0003369091],"domain_scores_gemma":[0.9989159,0.00001919339,0.0002863968,0.0004959756,0.0001321472,0.0001504103],"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":[0.004255416,0.01070529,0.2277186,0.0227753,0.02121419,0.00007509501,0.04975076,0.004783984,0.08697294,0.004554448,0.1379279,0.4292661],"study_design_scores_gemma":[0.004209244,0.002091432,0.07348914,0.0002985903,0.002560288,0.0002668444,0.01935725,0.1355239,0.1665554,0.0009026467,0.5918979,0.002847349],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6993409,0.005261891,0.2781874,0.0005671975,0.001849995,0.001177386,0.0002563623,0.0004484268,0.01291048],"genre_scores_gemma":[0.7975474,0.0002250225,0.1996225,0.001288885,0.0002005082,0.00007543276,0.0005737812,0.00003333834,0.0004331075],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.45397,"threshold_uncertainty_score":0.8016279,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0833120254126558,"score_gpt":0.309137849162802,"score_spread":0.2258258237501461,"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."}}