{"id":"W2156124616","doi":"10.1093/bioinformatics/btm452","title":"Leveraging the structure of the Semantic Web to enhance information retrieval for proteomics","year":2007,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institutes of Health; National Institute of General Medical Sciences; McGill University","keywords":"Computer science; Information retrieval; Graph; Leverage (statistics); SPARQL; RDF; Subgraph isomorphism problem; Semantic Web; Artificial intelligence; Theoretical computer science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000353229,0.0000869967,0.00008684798,0.00002671034,0.0001104725,0.00002093344,0.0003008519,0.0001214386,0.000001648103],"category_scores_gemma":[0.0005593041,0.00004714064,0.00006175328,0.0001384081,0.00009430789,0.000007094738,0.0001137386,0.00007800209,0.000002171912],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001134614,"about_ca_system_score_gemma":0.00007864265,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000212983,"about_ca_topic_score_gemma":0.00001002011,"domain_scores_codex":[0.9992741,0.000009278537,0.0003279088,0.00005380419,0.0001512035,0.0001837125],"domain_scores_gemma":[0.9993715,0.00003449891,0.0001720707,0.0002842938,0.0001030779,0.00003459337],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005882849,0.00003301783,0.00136342,0.0006748022,0.0001215066,1.508906e-7,0.007123276,0.0003069863,0.841459,0.0003866384,0.01706722,0.1308756],"study_design_scores_gemma":[0.000253139,0.0001803504,0.001606853,0.00004138251,0.00001654793,0.000008379809,0.001243815,0.002040811,0.9135681,0.0001096452,0.08080287,0.0001280813],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8570207,0.00003868761,0.1411855,0.000618278,0.0002909339,0.0005967266,0.00004403594,0.00001078136,0.0001943303],"genre_scores_gemma":[0.9691372,0.000008122308,0.02976422,0.000906106,0.00008631632,0.00000234766,0.000023878,0.000004606378,0.00006719178],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1307476,"threshold_uncertainty_score":0.1922339,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009301903491705083,"score_gpt":0.2663208881146019,"score_spread":0.2570189846228968,"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."}}