{"id":"W2147873108","doi":"10.1093/bib/bbn052","title":"Building biomedical web communities using a semantically aware content management system","year":2008,"lang":"en","type":"article","venue":"Briefings in Bioinformatics","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; World Wide Web; Content management system; Content management; Semantic Web; Information retrieval","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":[0.0003209221,0.0002005108,0.0002713871,0.0001496537,0.0002002349,0.00003184715,0.0003520797,0.0002391,0.00000460794],"category_scores_gemma":[0.000056165,0.0001780315,0.00008483778,0.0001868076,0.0004935212,0.00000803018,0.0003668229,0.0001780096,0.000008326894],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005878706,"about_ca_system_score_gemma":0.00007860077,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001403631,"about_ca_topic_score_gemma":0.0000148127,"domain_scores_codex":[0.9985477,0.00004511169,0.0005775273,0.0001234453,0.0002944677,0.0004117253],"domain_scores_gemma":[0.9993222,0.00003184455,0.0001354731,0.0003415984,0.00005832149,0.0001105789],"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":[0.002457207,0.00509559,0.1035223,0.03032964,0.004690854,0.003694827,0.04973126,0.002300435,0.174498,0.05071265,0.125386,0.4475812],"study_design_scores_gemma":[0.01258712,0.001766024,0.01319597,0.005351608,0.0002557639,0.006211661,0.07758886,0.4500066,0.0118167,0.0001646508,0.4176066,0.003448483],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9131855,0.0002654793,0.08480749,0.0002661335,0.0002101069,0.0002355496,0.0000224799,0.0000923659,0.000914924],"genre_scores_gemma":[0.8621593,0.0002403531,0.1366064,0.0007697788,0.0000662643,0.00001493475,0.00005266528,0.00001847115,0.00007186741],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4477062,"threshold_uncertainty_score":0.7259914,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05299525054453662,"score_gpt":0.2724175806396286,"score_spread":0.2194223300950919,"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."}}