{"id":"W2133525847","doi":"10.1093/bioinformatics/btl235","title":"Integrating image data into biomedical text categorization","year":2006,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":118,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Xerox Foundation","keywords":"Computer science; Annotation; Categorization; Information retrieval; Task (project management); Text categorization; Feature (linguistics); Artificial intelligence; Natural language processing","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001121999,0.0009312805,0.0009563782,0.009778846,0.0005494352,0.002275308,0.001083211,0.001498062,0.003156756],"category_scores_gemma":[0.005427944,0.0003339002,0.0009207996,0.005108669,0.0006750115,0.003069703,0.001324425,0.0009115087,0.003024717],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005417443,"about_ca_system_score_gemma":0.0005725572,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00205477,"about_ca_topic_score_gemma":0.002765812,"domain_scores_codex":[0.9990363,0.0001610412,0.0001047904,0.0002520368,0.0003653468,0.00008048799],"domain_scores_gemma":[0.9963206,0.001457968,0.0003180051,0.0006578663,0.001084229,0.0001613535],"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":[0.0002868091,0.0004030759,0.006022189,0.0007212918,0.0001084638,0.0003440718,0.0001956398,0.00362822,0.09358793,0.003519163,0.0116968,0.8794864],"study_design_scores_gemma":[0.0001707917,0.0009899054,0.05364998,0.0005182867,0.0006003064,0.00262939,0.001567765,0.3942491,0.3265924,0.09884266,0.1198579,0.0003314947],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1088406,0.006781992,0.8450664,0.002928699,0.0005825154,0.000953729,0.009363782,0.01357989,0.0119024],"genre_scores_gemma":[0.1686904,0.001998269,0.8128728,0.000404457,0.0004850869,0.0003342135,0.01114656,0.0003189728,0.00374928],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009778846,"threshold_uncertainty_score":0.01056039,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01574011550689704,"score_gpt":0.2775507319346824,"score_spread":0.2618106164277854,"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."}}