{"id":"W2007622742","doi":"10.1093/bib/bbr003","title":"Building an HIV data mashup using Bio2RDF","year":2011,"lang":"en","type":"article","venue":"Briefings in Bioinformatics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Canadian Institutes of Health Research","keywords":"Human immunodeficiency virus (HIV); Mashup; Computer science; Data integration; Expression (computer science); Data mining; Computational biology; World Wide Web; Biology; Virology; Web service; Programming language","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005589132,0.0002355313,0.0002084421,0.0001118879,0.0001043552,0.00007209821,0.0009082245,0.0002497119,0.00002830282],"category_scores_gemma":[0.00005381636,0.0002352962,0.00004579464,0.0001763338,0.0001119849,0.00007344745,0.0007040832,0.0001734111,0.00001979368],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002346476,"about_ca_system_score_gemma":0.00009775205,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001351186,"about_ca_topic_score_gemma":0.00004751011,"domain_scores_codex":[0.9984442,0.00002167138,0.0006575006,0.0002625254,0.0001521074,0.0004620197],"domain_scores_gemma":[0.9984017,0.000007619718,0.0002407293,0.001168047,0.00004931681,0.0001326133],"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":[0.001670609,0.002787142,0.04562755,0.002610614,0.001266406,0.0001384934,0.03341044,0.007646161,0.1419106,0.05801192,0.1072275,0.5976925],"study_design_scores_gemma":[0.002224694,0.0003813055,0.002265187,0.0002107657,0.00007860686,0.000282058,0.001220123,0.8751533,0.01399843,0.002924018,0.0996206,0.001640914],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6152983,0.0004825199,0.3699583,0.0001138041,0.0005344494,0.0006319915,0.0002252099,0.00008216578,0.01267331],"genre_scores_gemma":[0.2937773,0.0001540258,0.7029082,0.002112122,0.0002494207,0.000006222479,0.0006524584,0.00005274736,0.00008754298],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8675072,"threshold_uncertainty_score":0.9595098,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0617947955621351,"score_gpt":0.2800292266842744,"score_spread":0.2182344311221393,"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."}}